SlideShare a Scribd company logo
1 of 4
Download to read offline
International Journal of Research in Engineering and Science (IJRES)
ISSN (Online): 2320-9364, ISSN (Print): 2320-9356
www.ijres.org Volume 1 Issue 2 ǁ June 2013 ǁ PP.41-44
www.ijres.org 41 | Page
A New Approach for Vehicle Detection in Aerial Surveillances
Divya Michael1
, Paul P Mathai2
1
(Dept. of Computer Science & Engineering, FISAT Angamaly, M. G. University, India)
2
(Dept. of Computer Science & Engineering, FISAT Angamaly, M. G. University, India)
Abstract— Target object detection in aerial surveillance using image processing techniques is growing more
and more important. Aerial surveillance is more suitable for monitoring fast-moving targets and covers a much
larger spatial area. Aerial view has the advantage of providing a better perspective of the area being covered.
These techniques make use of the aerial videos taken from aerial vehicles. Vehicle detection technique has a
variety of applications, such as traffic management, police and military. The purpose of this technical report is
to provide a survey of research related to the application of vehicle detection techniques for traffic management
and other applications and also to present the proposed system.
Index Terms—Aerial surveillance, Background subtraction, Feature extraction, Gaussian mixture, SVM,
Vehicle classification, Vehicle detection.
I. INTRODUCTION
In recent years, the analysis of aerial videos taken from aerial vehicle has become an important issue.
The increase in the number of vehicles on roadway networks has led transport management agencies to allow
use of technology advances resulting in better decisions. According to this perspective aerial surveillance has
better place nowadays. Aerial surveillance is more suitable for providing increased monitoring results in case of
fast-moving targets and covers a much larger spatial area. So these aerial surveillance systems become excellent
supplements of ground-plane surveillance systems. One of the main topics in intelligent aerial surveillance is
vehicle detection and tracking. The difficulties involved in the aerial Surveillance include the camera motions
such as rotation, panning and tilting. Also the different camera heights largely affect the detection results. Vision
based techniques is one of the most common approach to analyze vehicles from images or videos.
In this system, design a pixelwise classification method for vehicle detection and novelty lies in the
fact that, instead of performing pixelwise classification, relationship among the neighboring pixels in a
particular region are preserved in the feature extraction process. The vehicle detection technique consist of three
parts i.e., a background subtraction model, vehicle detection and tracking.
In this system, design a pixelwise classification method for vehicle detection and novelty lies in the
fact that, instead of performing pixelwise classification, relationship among the neighboring pixels in a
particular region are preserved in the feature extraction process. The vehicle detection technique consist of three
parts i.e., a background subtraction model, vehicle detection and tracking.
The Gaussian mixture algorithm used for background subtraction developed by Stauffer and Grimson
[1] aims to segment moving foreground objects from relatively stationary objects. Recently pixel-based
probabilistic model methods gained lots of interests and have shown good detection results. The values of a
particular pixel are modeled as a mixture of adaptive Gaussians. At each iteration Gaussians are evaluated using
a simple heuristic to determine which ones are mostly likely to correspond to the background pixels. Those
pixels that do not match with the background Gaussians are classified as foreground.
In the detection phase, first perform background color removal. Afterward, the feature extraction
procedure is performed as in the training phase. The extracted features from feature extraction phase serve as the
input to support vector machine, which indicates whether a pixel belongs to a vehicle or not. There is no need to
generate multiscale sliding windows either. The detection task is based on pixel wise classification and the
features are extracted in a neighborhood region of each pixel. Finally, the extracted features comprise of pixel-
level information and also the relationship among neighboring pixels in a region. This design is more effective
and efficient than region-based or multiscale sliding window detection methods.
This system considers features including local feature points, vehicle colors and edges. For vehicle
color extraction, system utilizes color transform to separate vehicle colors and non-vehicle colors effectively.
For edge detection, system applies moment-preserving method to adjust the thresholds for canny edge detector
automatically, which in turn increases the adaptability and accuracy of the system. A support vector machine is
A New Approach for Vehicle Detection in Aerial Surveillances
www.ijres.org 42 | Page
constructed for classification purpose and features are extracted. The extracted features comprise not only pixel-
level information but also relationship among neighboring pixels in a region is considered.
This paper mentions various methodologies for target object detection in aerial surveillance Section 2
summarizes important works related to vehicle detection and also presented the proposed system and section 3
concludes the discussion.
II. RELATED WORK
S. Srinivasan, H. Latchman, J. Shea, T. Wong, and J. McNair [2] have introduced a method for the
airborne video registration and traffic-flow parameter estimation which focus on airborne helicopter video for
estimating traffic parameters. The airborne video is taken from a digital video camera attached to the skid of the
helicopter. Roll, pitch, and yaw of the helicopter make the video difficult to view, unstable and the derived
parameters less accurate. To avoid this, a frame-by-frame video-registration technique using a feature tracker to
automatically determine control-point correspondences is present in the system. This feature tracker is used to
track fixed features through the sequence of images in the video. These feature-location correspondences are
used as control points to compute a polynomial transformation function to warp every frame in the sequence
successively to the reference. The reference frame can be any one of the frames in the video segment. By
changing the tracking-window orientation, the same feature tracker can be used to track moving vehicles within
the registered video segment. The advantages of the system include excessive reliance on sensor data,
inadequate stabilization and availability of elevation maps.
S. Hinz and A. Baumgartner [3] utilized a hierarchical model that describes different levels of details of
vehicle features. In this system, vehicles are represented in different levels and details of vehicle are increasing
from left to right. Hierarchical model describes the appearance of vehicle at different levels. At lowest level
vehicle substructures are described like type of vehicle and last level represents 3D information and local context
of vehicle. The advantage of this system is that it neither relies on external information like digital map or site
models, nor it is limited to any specific vehicle models. As disadvantage, the system would miss vehicles when
the contrast is weak or when the influences of neighboring objects are present.
H. Cheng and J.Wus [4] have introduced an adaptive ROI estimation algorithm by analyzing the way
the camera is operated. The system proposes an ego-motion of the camera computed from adjacent video
frames, and deriving the ROI defined by the operator while capturing the video using an adaptive operator
attention model. When monitoring a building beside a highway, only those moving objects associated with the
building, such as objects entering or leaving the building, are of interest. Therefore, accurate ROI estimation for
aerial surveillance applications must be adaptive and cannot always use the same type of objects, such as
moving objects as ROI.
Lin et al [5] proposed a method by subtracting background colors of each frame and then refined
vehicle candidate regions by enforcing size constraints of vehicles. The disadvantage of the system is that, it
assumed too many parameters such as the smallest and largest sizes of vehicles, height and the focus of the
airborne camera etc. Assuming these parameters as known priors might not be realistic in real applications. Also
the vehicle detection method is based on cascade classifiers.
Cheng and Butler [6] considered multiple clues and used a mixture of experts to merge the clues for
vehicle detection in aerial images. The system consists of color segmentation via mean-shift algorithm and
motion analysis via change detection. Also they presented a trainable sequential maximum a posterior method
for multiscale analysis and enforcement of contextual information. The system contains a method by subtracting
background colors of each frame and then refined vehicle candidate regions by enforcing size constraints of
vehicles. A large number of positive and negative training samples need to be collected for the training purpose.
Also considers multiscale sliding windows which are generated at the detection stage. The main demerit of this
method is that there are a lot of miss detections on rotated vehicles. The faces with poses are easily missed if
only frontal faces are trained. Also if faces with poses are added as positive samples, then the number of false
alarms would surge.
J. Y. Choi and Y. K. Yang [7] have proposed a vehicle detection algorithm using the symmetric
property of car shapes. But this is prone to false detections such as symmetrical details of buildings or road
markings. In order to avoid this, a log-polar histogram shape descriptor is used to verify the shape of the
candidates. The shape descriptor is obtained from a fixed vehicle model, making the algorithm inflexible. Also
the algorithm relied on mean-shift clustering algorithm for image color segmentation. Moreover, nearby
vehicles might be clustered as one region if they have similar colors. The advantage is that, using symmetric
property of car shapes, object can easily detect. Disadvantages include a vehicle tends to be separated as many
regions since car roofs and windshields usually have different colors. Also the high computational complexity of
mean-shift segmentation algorithm is another concern.
A New Approach for Vehicle Detection in Aerial Surveillances
www.ijres.org 43 | Page
Luo-weitsai, Jun-weihsieh, Kuo-chin fan [8] proposed a novel vehicle detection method using color
transform model. The detection procedure consists of different stages. In the first stage a color transformation
model is used to separate vehicle colors from nonvehicle colors effectively. This color model transforms
(R,G,B) color components into the color domain (u,v). The technique which is adopted for this is dimensionality
reduction. Beginning of the technique follows the collection of several thousands of training images and these
training images are collected by projecting all colors of input pixels on the color space. The classification is
carried out by means of a Bayesian classifier. By using this method the authors insist that vehicles can be very
robustly and accurately verified and detected from static images.
B. Morris and M. Trivedi [9] proposed a tracking system with ability to classify vehicles into three
classes such as sedan, semi, truck+SUV+van. This system was developed after comparing classification
schemes using vehicle images and measurements. The most accurate among this learned classifier was
integrated into tracking software which greatly improved the accuracy on low resolution traffic video.
J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu [10] proposed a vehicle tracking and classification
technique to estimate important traffic parameters from video sequences using one camera. The system has the
ability to categorize vehicles into more specific classes by introducing a new linearity feature in vehicle
representation. Also this system can easily tackle the problem of vehicle occlusion caused by shadows. This
problem is solved by shadow algorithm that uses a set of lines to eliminate all unwanted shadows.
Background subtraction is a process which aims to segment moving foreground objects from a
relatively stationary background. Recently pixel-based probabilistic model methods gained lots of interests and
have shown good detection results. Background modeling by Gaussian mixtures is a pixel based process. Let x
be a random process representing the value of a given pixel in time. A convenient framework to model the
probability density function of x is the parametric Gaussian mixture model where the density is composed of a
sum of Gaussians. Let p(x) denotes the probability density function of a Gaussian mixture comprising K
component densities. The mixture of Gaussians algorithm, proposed by Stauffer and Grimson [1], estimates
these parameters over time to obtain a robust representation of the background.Here every pixel is checked
against K Gaussian distributions until a match is found and if no match is found for a pixel, the least probable
distribution is replaced with a distribution with current value as its mean value. The advantage includes its good
accuracy and disadvantages include high computational cost and high execution time.
Zivkovic [11] proposed an approach which reduces the computation time and memory bandwidth.
Here, a Bayesian approach is formulated to select the required number of Gaussian modes for each pixel in the
scene. In scenes with static background, this approach assigns a single mode Gaussian to model most of the
pixels which helps to reduce average processing time by 32%.However in the outdoor video (trees sequence),
results show only a 2% improvement since a significantly large portion of the scene requires a multimodal
model. In the GMM algorithm the weights of the Gaussian mixture represent the fraction of the data samples
‗x(t)‘ that belongs to the particular mode in the model. This system used a Dirichlet prior with negative
coefficients. This is done with an intention of accepting a class only if there is enough evidence from the data
samples for the existence of the class. Advantages include fast compared to GMM, efficient and accurate.
Disadvantage includes reduced computation time.
D.S. Lee [12] proposed an effective gaussian mixture learning for video background subtraction, each
pixel in a frame is modeled as a stochastic process and the modes of the GMM are arranged in decreasing order
of their weights. A predefined fraction of the weights proposed to use modes with low weights to model the
foreground and higher weight to be selected as the background. A match of an incoming pixel to any of the
modes defines the pixel as background or foreground. Advantages are fast compared to GMM and makes fast
learning. Disadvantages include high memory requirement
P. Gorur and B. Amrutur [13] proposed a speeded up gaussian mixture model algorithm for background
subtraction combines fast learning of effective GMM with automatic selection of number of modes in improved
GMM to obtain a high efficient and accurate scheme. This concept is a modification to his adaptive GMM
algorithm that further reduces execution time by replacing expensive floating point computations with low cost
integer operations. In order to maintain accuracy, derive a heuristic that computes periodic floating point
updates for the GMM weight parameter using the value of an integer counter. Limitations of GMM includes
good accuracy of GMM comes at high computational cost, slow learning rate and GMM with more modes is
slower compared to single gaussian scheme. Advantages of speeded up GMM include computation time
reduction, fast learning and highly efficient and accurate.
A. Proposed Method
The proposed system presents a new vehicle detection framework that preserves the advantages of the
existing works and avoids their drawbacks. The vehicle detection frame work can be classified into training
phase and the detection phase [17]. In the training phase, it is possible to extract multiple features including
A New Approach for Vehicle Detection in Aerial Surveillances
www.ijres.org 44 | Page
local edge and corner features. In the detection phase, perform background color removal using speeded up
GMM. This speeded up gaussian mixture model algorithm for background subtraction combines fast learning of
effective GMM with automatic selection of number of modes in improved GMM to obtain a high efficient and
accurate scheme. Then features are extracted in the training phase. The proposed system consist of a new color
model to separate vehicle colors from non-vehicle colors effectively and this color model transforms (R,G,B)
color components into the color domain (u,v). In this (u,v) color model vehicle colors and nonvehicle colors
have less overlapping regions .The support vector machine (SVM) is used to classify vehicle colors and non-
vehicle colors effectively. A support vector machine is again used for classification of vehicle and non-vehicle
pixel.
III. CONCLUSION
This paper has focused on important methods to detect vehicles from different video sequences. This
vehicle detection system escapes from the existing frameworks of vehicle detection in aerial surveillance, which
is either region based or sliding window based. Instead a pixel wise classification method for vehicle detection
is proposed. This method checks the relations among neighboring pixels in a region which are preserved in the
feature extraction process. Features extracted comprise not only pixel-level information but also region-level
information. Finally classify vehicle colors and non-vehicle colors effectively using support vector machine.
REFERENCES
[1] C. Stauffer and W. Grimson. ―Adaptive background mixture models for real-time tracking.‖ IEEE
Conf. on Computer Vision and Patteren Recognition, vol. 2,pp. 246-252, June 1999.
[2] S. Srinivasan, H. Latchman, J. Shea, T. Wong, and J.Mc Nair, ―Airborne traffic surveillance systems:
Video surveillance of highway traffic,‖ in Pro.ACm 2nd
Int. Workshop Video Surveillance
Sens.Netw., 2004,pp. 131-135
[3] S. S. Hinz and A. Baumgartner, ―Vehicle detection in aerial images using generic features, grouping,
and context,‖ in Proc. DAGM-Symp., Sep.2001, vol. 2191, Lecture Notes in Computer Science, pp.
45–52.
[4] H. Cheng and J.Wus, ―Adaptive region of interest estimation for aerial surveillance video,‖ in Proc.
IEEE Int. Conf. Image Process., 2005, vol. 3, pp. 860–863.
[5] R. Lin, X. Cao, Y. Xu, C.Wu, and H. Qiao, ―Airborne moving vehicle detection for urban traffic
surveillance,‖ in Proc. 11th Int. IEEE Conf. Intell. Transp. Syst., Oct. 2008, pp. 163–167
[6] H. Cheng and D. Butler, ―Segmentation of aerial surveillance video using a mixture of experts,‖ in
Proc. IEEE Digit. Imaging Comput.—Tech. Appl., 2005, p. 66.
[7] J. Y. Choi and Y. K. Yang, ―Vehicle detection from aerial images using local shape information,‖ Adv.
Image Video Technol., vol. 5414, Lecture Notes in Computer Science, pp. 227–236, Jan. 2009.
[8] L. W. Tsai, J. W. Hsieh, and K. C. Fan, ―Vehicle detection using normalized color and edge map,‖
IEEE Trans. Image Process., vol. 16, no.3, pp. 850–864, Mar. 2007.
[9] B. Morris and M. Trivedi, ―Robust classification and tracking of vehicles in traffic video streams,‖ in
Proc. IEEE ITSC, 2006, pp. 1078–1083.
[10] J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu, ―Automatic traffic surveillance system for vehicle
tracking and classification,‖ IEEE Trans.Intell. Transp. Syst., vol. 7, no. 2, pp. 175–187, Jun. 2006.
[11] Z. Zivkovic.‖ Improved adaptive gaussian mixture model for background subtraction.‖Int‘l Conf. on
Pattern Recognition, 2:28–31, 2004.
[12] D.-S. Lee. ―Effective gaussian mixture learning for video background subtraction.‖ IEEE Trans. on
Pattern Analysis and Ma-chine Intelligence, 27:827–832, 2005.
[13] P. Gorur and B. Amrutur. Speeded up gaussian mixture model algorithm for background subtraction. In
IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pages
386–391, Sept. 2011.
[14] C. G. Harris and M. J. Stephens, ―A combined corner and edge detector,‖inProc. 4th Alvey Vis. Conf.,
1988, pp. 147–151.
[15] J. F. Canny, ―A computational approach to edge detection,‖ IEEE Trans. Pattern Anal. Mach. Intell.,
vol. PAMI-8, no. 6, pp. 679–698,Nov. 1986.
[16] W. H. Tsai, ―Moment-preserving thresholding: A new approach,‖Comput. Vis.Graph., Image Process.,
vol. 29, no. 3, pp. 377–393, 1985.
[17] Hsu-Yung Cheng, Chih-Chia Weng, and Yi-Ying Chen ―Vehicle Detection in Aerial urveillance Using
Dynamic Bayesian Networks‖ IEEE Trans. On Image Processing, Vol. 21, No. 4, April 2012.

More Related Content

What's hot

Review on moving vehicle detection in aerial surveillance
Review on moving vehicle detection in aerial surveillanceReview on moving vehicle detection in aerial surveillance
Review on moving vehicle detection in aerial surveillanceeSAT Publishing House
 
IRJET- Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...
IRJET-  	  Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...IRJET-  	  Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...
IRJET- Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...IRJET Journal
 
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...aciijournal
 
Real time deep-learning based traffic volume count for high-traffic urban art...
Real time deep-learning based traffic volume count for high-traffic urban art...Real time deep-learning based traffic volume count for high-traffic urban art...
Real time deep-learning based traffic volume count for high-traffic urban art...Conference Papers
 
Scenario-Based Development & Testing for Autonomous Driving
Scenario-Based Development & Testing for Autonomous DrivingScenario-Based Development & Testing for Autonomous Driving
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
 
Video Surveillance Systems For Traffic Monitoring
Video Surveillance Systems For Traffic MonitoringVideo Surveillance Systems For Traffic Monitoring
Video Surveillance Systems For Traffic MonitoringMeridian Media
 
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...IRJET Journal
 
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...ijcsit
 
Density of route frequency for enforcement
Density of route frequency for enforcement Density of route frequency for enforcement
Density of route frequency for enforcement Conference Papers
 
IRJET- Vehicle Number Plate Recognition System
IRJET- Vehicle Number Plate Recognition SystemIRJET- Vehicle Number Plate Recognition System
IRJET- Vehicle Number Plate Recognition SystemIRJET Journal
 
Traffic Violation Detection Using Multiple Trajectories of Vehicles
Traffic Violation Detection Using Multiple Trajectories of VehiclesTraffic Violation Detection Using Multiple Trajectories of Vehicles
Traffic Violation Detection Using Multiple Trajectories of VehiclesIJERA Editor
 
VDIS: A System for Morphological Detection and Identification of Vehicles in ...
VDIS: A System for Morphological Detection and Identification of Vehicles in ...VDIS: A System for Morphological Detection and Identification of Vehicles in ...
VDIS: A System for Morphological Detection and Identification of Vehicles in ...rsmahabir
 
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...cscpconf
 
Implementation of a lane-tracking system for autonomous driving using Kalman ...
Implementation of a lane-tracking system for autonomous driving using Kalman ...Implementation of a lane-tracking system for autonomous driving using Kalman ...
Implementation of a lane-tracking system for autonomous driving using Kalman ...Francesco Corucci
 
Obstacle Detection and Collision Avoidance System
Obstacle Detection and Collision Avoidance SystemObstacle Detection and Collision Avoidance System
Obstacle Detection and Collision Avoidance SystemIRJET Journal
 
Real time pedestrian detection with deformable part models [h. cho, p. rybski...
Real time pedestrian detection with deformable part models [h. cho, p. rybski...Real time pedestrian detection with deformable part models [h. cho, p. rybski...
Real time pedestrian detection with deformable part models [h. cho, p. rybski...tino
 
IRJET- Reckoning the Vehicle using MATLAB
IRJET- Reckoning the Vehicle using MATLABIRJET- Reckoning the Vehicle using MATLAB
IRJET- Reckoning the Vehicle using MATLABIRJET Journal
 
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal OneRobot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal Oneijcsit
 

What's hot (19)

A0140109
A0140109A0140109
A0140109
 
Review on moving vehicle detection in aerial surveillance
Review on moving vehicle detection in aerial surveillanceReview on moving vehicle detection in aerial surveillance
Review on moving vehicle detection in aerial surveillance
 
IRJET- Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...
IRJET-  	  Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...IRJET-  	  Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...
IRJET- Robust and Fast Detection of Moving Vechiles in Aerial Videos usin...
 
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...
AN EFFICIENT SYSTEM FOR FORWARD COLLISION AVOIDANCE USING LOW COST CAMERA & E...
 
Real time deep-learning based traffic volume count for high-traffic urban art...
Real time deep-learning based traffic volume count for high-traffic urban art...Real time deep-learning based traffic volume count for high-traffic urban art...
Real time deep-learning based traffic volume count for high-traffic urban art...
 
Scenario-Based Development & Testing for Autonomous Driving
Scenario-Based Development & Testing for Autonomous DrivingScenario-Based Development & Testing for Autonomous Driving
Scenario-Based Development & Testing for Autonomous Driving
 
Video Surveillance Systems For Traffic Monitoring
Video Surveillance Systems For Traffic MonitoringVideo Surveillance Systems For Traffic Monitoring
Video Surveillance Systems For Traffic Monitoring
 
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...
License Plate Recognition System for Moving Vehicles Using ­Laplacian Edge De...
 
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...
REVIEW OF LANE DETECTION AND TRACKING ALGORITHMS IN ADVANCED DRIVER ASSISTANC...
 
Density of route frequency for enforcement
Density of route frequency for enforcement Density of route frequency for enforcement
Density of route frequency for enforcement
 
IRJET- Vehicle Number Plate Recognition System
IRJET- Vehicle Number Plate Recognition SystemIRJET- Vehicle Number Plate Recognition System
IRJET- Vehicle Number Plate Recognition System
 
Traffic Violation Detection Using Multiple Trajectories of Vehicles
Traffic Violation Detection Using Multiple Trajectories of VehiclesTraffic Violation Detection Using Multiple Trajectories of Vehicles
Traffic Violation Detection Using Multiple Trajectories of Vehicles
 
VDIS: A System for Morphological Detection and Identification of Vehicles in ...
VDIS: A System for Morphological Detection and Identification of Vehicles in ...VDIS: A System for Morphological Detection and Identification of Vehicles in ...
VDIS: A System for Morphological Detection and Identification of Vehicles in ...
 
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...
 
Implementation of a lane-tracking system for autonomous driving using Kalman ...
Implementation of a lane-tracking system for autonomous driving using Kalman ...Implementation of a lane-tracking system for autonomous driving using Kalman ...
Implementation of a lane-tracking system for autonomous driving using Kalman ...
 
Obstacle Detection and Collision Avoidance System
Obstacle Detection and Collision Avoidance SystemObstacle Detection and Collision Avoidance System
Obstacle Detection and Collision Avoidance System
 
Real time pedestrian detection with deformable part models [h. cho, p. rybski...
Real time pedestrian detection with deformable part models [h. cho, p. rybski...Real time pedestrian detection with deformable part models [h. cho, p. rybski...
Real time pedestrian detection with deformable part models [h. cho, p. rybski...
 
IRJET- Reckoning the Vehicle using MATLAB
IRJET- Reckoning the Vehicle using MATLABIRJET- Reckoning the Vehicle using MATLAB
IRJET- Reckoning the Vehicle using MATLAB
 
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal OneRobot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One
Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One
 

Similar to F124144

traffic jam detection using image processing
traffic jam detection using image processingtraffic jam detection using image processing
traffic jam detection using image processingMalika Alix
 
Real Time Object Identification for Intelligent Video Surveillance Applications
Real Time Object Identification for Intelligent Video Surveillance ApplicationsReal Time Object Identification for Intelligent Video Surveillance Applications
Real Time Object Identification for Intelligent Video Surveillance ApplicationsEditor IJCATR
 
Application of improved you only look once model in road traffic monitoring ...
Application of improved you only look once model in road  traffic monitoring ...Application of improved you only look once model in road  traffic monitoring ...
Application of improved you only look once model in road traffic monitoring ...IJECEIAES
 
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSISVEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSISIRJET Journal
 
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...IRJET Journal
 
Feature Selection Method For Single Target Tracking Based On Object Interacti...
Feature Selection Method For Single Target Tracking Based On Object Interacti...Feature Selection Method For Single Target Tracking Based On Object Interacti...
Feature Selection Method For Single Target Tracking Based On Object Interacti...IJERA Editor
 
A Review: Machine vision and its Applications
A Review: Machine vision and its ApplicationsA Review: Machine vision and its Applications
A Review: Machine vision and its ApplicationsIOSR Journals
 
Paper id 25201468
Paper id 25201468Paper id 25201468
Paper id 25201468IJRAT
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...aciijournal
 
Automatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels
Automatic Detection of Unexpected Accidents Monitoring Conditions in TunnelsAutomatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels
Automatic Detection of Unexpected Accidents Monitoring Conditions in TunnelsIRJET Journal
 
Leader follower formation control of ground vehicles using camshift based gui...
Leader follower formation control of ground vehicles using camshift based gui...Leader follower formation control of ground vehicles using camshift based gui...
Leader follower formation control of ground vehicles using camshift based gui...ijma
 
Vigilance: Vehicle Detector and Tracker
Vigilance: Vehicle Detector and TrackerVigilance: Vehicle Detector and Tracker
Vigilance: Vehicle Detector and TrackerIRJET Journal
 
An Analysis of Various Deep Learning Algorithms for Image Processing
An Analysis of Various Deep Learning Algorithms for Image ProcessingAn Analysis of Various Deep Learning Algorithms for Image Processing
An Analysis of Various Deep Learning Algorithms for Image Processingvivatechijri
 
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATION
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATIONFRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATION
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATIONsipij
 

Similar to F124144 (20)

A real-time system for vehicle detection with shadow removal and vehicle clas...
A real-time system for vehicle detection with shadow removal and vehicle clas...A real-time system for vehicle detection with shadow removal and vehicle clas...
A real-time system for vehicle detection with shadow removal and vehicle clas...
 
traffic jam detection using image processing
traffic jam detection using image processingtraffic jam detection using image processing
traffic jam detection using image processing
 
Real Time Object Identification for Intelligent Video Surveillance Applications
Real Time Object Identification for Intelligent Video Surveillance ApplicationsReal Time Object Identification for Intelligent Video Surveillance Applications
Real Time Object Identification for Intelligent Video Surveillance Applications
 
Application of improved you only look once model in road traffic monitoring ...
Application of improved you only look once model in road  traffic monitoring ...Application of improved you only look once model in road  traffic monitoring ...
Application of improved you only look once model in road traffic monitoring ...
 
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSISVEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS
VEHICLE DETECTION USING YOLO V3 FOR COUNTING THE VEHICLES AND TRAFFIC ANALYSIS
 
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...
VEHICLE DETECTION, CLASSIFICATION, COUNTING, AND DETECTION OF VEHICLE DIRECTI...
 
Feature Selection Method For Single Target Tracking Based On Object Interacti...
Feature Selection Method For Single Target Tracking Based On Object Interacti...Feature Selection Method For Single Target Tracking Based On Object Interacti...
Feature Selection Method For Single Target Tracking Based On Object Interacti...
 
A Review: Machine vision and its Applications
A Review: Machine vision and its ApplicationsA Review: Machine vision and its Applications
A Review: Machine vision and its Applications
 
Paper id 25201468
Paper id 25201468Paper id 25201468
Paper id 25201468
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
An Efficient System for Forward Collison Avoidance Using Low Cost Camera & Em...
 
Automatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels
Automatic Detection of Unexpected Accidents Monitoring Conditions in TunnelsAutomatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels
Automatic Detection of Unexpected Accidents Monitoring Conditions in Tunnels
 
Leader follower formation control of ground vehicles using camshift based gui...
Leader follower formation control of ground vehicles using camshift based gui...Leader follower formation control of ground vehicles using camshift based gui...
Leader follower formation control of ground vehicles using camshift based gui...
 
Vigilance: Vehicle Detector and Tracker
Vigilance: Vehicle Detector and TrackerVigilance: Vehicle Detector and Tracker
Vigilance: Vehicle Detector and Tracker
 
An Analysis of Various Deep Learning Algorithms for Image Processing
An Analysis of Various Deep Learning Algorithms for Image ProcessingAn Analysis of Various Deep Learning Algorithms for Image Processing
An Analysis of Various Deep Learning Algorithms for Image Processing
 
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATION
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATIONFRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATION
FRONT AND REAR VEHICLE DETECTION USING HYPOTHESIS GENERATION AND VERIFICATION
 

More from IJRES Journal

Exploratory study on the use of crushed cockle shell as partial sand replacem...
Exploratory study on the use of crushed cockle shell as partial sand replacem...Exploratory study on the use of crushed cockle shell as partial sand replacem...
Exploratory study on the use of crushed cockle shell as partial sand replacem...IJRES Journal
 
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...IJRES Journal
 
Review: Nonlinear Techniques for Analysis of Heart Rate Variability
Review: Nonlinear Techniques for Analysis of Heart Rate VariabilityReview: Nonlinear Techniques for Analysis of Heart Rate Variability
Review: Nonlinear Techniques for Analysis of Heart Rate VariabilityIJRES Journal
 
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...IJRES Journal
 
Study and evaluation for different types of Sudanese crude oil properties
Study and evaluation for different types of Sudanese crude oil propertiesStudy and evaluation for different types of Sudanese crude oil properties
Study and evaluation for different types of Sudanese crude oil propertiesIJRES Journal
 
A Short Report on Different Wavelets and Their Structures
A Short Report on Different Wavelets and Their StructuresA Short Report on Different Wavelets and Their Structures
A Short Report on Different Wavelets and Their StructuresIJRES Journal
 
A Case Study on Academic Services Application Using Agile Methodology for Mob...
A Case Study on Academic Services Application Using Agile Methodology for Mob...A Case Study on Academic Services Application Using Agile Methodology for Mob...
A Case Study on Academic Services Application Using Agile Methodology for Mob...IJRES Journal
 
Wear Analysis on Cylindrical Cam with Flexible Rod
Wear Analysis on Cylindrical Cam with Flexible RodWear Analysis on Cylindrical Cam with Flexible Rod
Wear Analysis on Cylindrical Cam with Flexible RodIJRES Journal
 
DDOS Attacks-A Stealthy Way of Implementation and Detection
DDOS Attacks-A Stealthy Way of Implementation and DetectionDDOS Attacks-A Stealthy Way of Implementation and Detection
DDOS Attacks-A Stealthy Way of Implementation and DetectionIJRES Journal
 
An improved fading Kalman filter in the application of BDS dynamic positioning
An improved fading Kalman filter in the application of BDS dynamic positioningAn improved fading Kalman filter in the application of BDS dynamic positioning
An improved fading Kalman filter in the application of BDS dynamic positioningIJRES Journal
 
Positioning Error Analysis and Compensation of Differential Precision Workbench
Positioning Error Analysis and Compensation of Differential Precision WorkbenchPositioning Error Analysis and Compensation of Differential Precision Workbench
Positioning Error Analysis and Compensation of Differential Precision WorkbenchIJRES Journal
 
Status of Heavy metal pollution in Mithi river: Then and Now
Status of Heavy metal pollution in Mithi river: Then and NowStatus of Heavy metal pollution in Mithi river: Then and Now
Status of Heavy metal pollution in Mithi river: Then and NowIJRES Journal
 
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...IJRES Journal
 
Experimental study on critical closing pressure of mudstone fractured reservoirs
Experimental study on critical closing pressure of mudstone fractured reservoirsExperimental study on critical closing pressure of mudstone fractured reservoirs
Experimental study on critical closing pressure of mudstone fractured reservoirsIJRES Journal
 
Correlation Analysis of Tool Wear and Cutting Sound Signal
Correlation Analysis of Tool Wear and Cutting Sound SignalCorrelation Analysis of Tool Wear and Cutting Sound Signal
Correlation Analysis of Tool Wear and Cutting Sound SignalIJRES Journal
 
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...IJRES Journal
 
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...IJRES Journal
 
A novel high-precision curvature-compensated CMOS bandgap reference without u...
A novel high-precision curvature-compensated CMOS bandgap reference without u...A novel high-precision curvature-compensated CMOS bandgap reference without u...
A novel high-precision curvature-compensated CMOS bandgap reference without u...IJRES Journal
 
Structural aspect on carbon dioxide capture in nanotubes
Structural aspect on carbon dioxide capture in nanotubesStructural aspect on carbon dioxide capture in nanotubes
Structural aspect on carbon dioxide capture in nanotubesIJRES Journal
 
Thesummaryabout fuzzy control parameters selected based on brake driver inten...
Thesummaryabout fuzzy control parameters selected based on brake driver inten...Thesummaryabout fuzzy control parameters selected based on brake driver inten...
Thesummaryabout fuzzy control parameters selected based on brake driver inten...IJRES Journal
 

More from IJRES Journal (20)

Exploratory study on the use of crushed cockle shell as partial sand replacem...
Exploratory study on the use of crushed cockle shell as partial sand replacem...Exploratory study on the use of crushed cockle shell as partial sand replacem...
Exploratory study on the use of crushed cockle shell as partial sand replacem...
 
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...
Congenital Malaria: Correlation of Umbilical Cord Plasmodium falciparum Paras...
 
Review: Nonlinear Techniques for Analysis of Heart Rate Variability
Review: Nonlinear Techniques for Analysis of Heart Rate VariabilityReview: Nonlinear Techniques for Analysis of Heart Rate Variability
Review: Nonlinear Techniques for Analysis of Heart Rate Variability
 
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...
Dynamic Modeling for Gas Phase Propylene Copolymerization in a Fluidized Bed ...
 
Study and evaluation for different types of Sudanese crude oil properties
Study and evaluation for different types of Sudanese crude oil propertiesStudy and evaluation for different types of Sudanese crude oil properties
Study and evaluation for different types of Sudanese crude oil properties
 
A Short Report on Different Wavelets and Their Structures
A Short Report on Different Wavelets and Their StructuresA Short Report on Different Wavelets and Their Structures
A Short Report on Different Wavelets and Their Structures
 
A Case Study on Academic Services Application Using Agile Methodology for Mob...
A Case Study on Academic Services Application Using Agile Methodology for Mob...A Case Study on Academic Services Application Using Agile Methodology for Mob...
A Case Study on Academic Services Application Using Agile Methodology for Mob...
 
Wear Analysis on Cylindrical Cam with Flexible Rod
Wear Analysis on Cylindrical Cam with Flexible RodWear Analysis on Cylindrical Cam with Flexible Rod
Wear Analysis on Cylindrical Cam with Flexible Rod
 
DDOS Attacks-A Stealthy Way of Implementation and Detection
DDOS Attacks-A Stealthy Way of Implementation and DetectionDDOS Attacks-A Stealthy Way of Implementation and Detection
DDOS Attacks-A Stealthy Way of Implementation and Detection
 
An improved fading Kalman filter in the application of BDS dynamic positioning
An improved fading Kalman filter in the application of BDS dynamic positioningAn improved fading Kalman filter in the application of BDS dynamic positioning
An improved fading Kalman filter in the application of BDS dynamic positioning
 
Positioning Error Analysis and Compensation of Differential Precision Workbench
Positioning Error Analysis and Compensation of Differential Precision WorkbenchPositioning Error Analysis and Compensation of Differential Precision Workbench
Positioning Error Analysis and Compensation of Differential Precision Workbench
 
Status of Heavy metal pollution in Mithi river: Then and Now
Status of Heavy metal pollution in Mithi river: Then and NowStatus of Heavy metal pollution in Mithi river: Then and Now
Status of Heavy metal pollution in Mithi river: Then and Now
 
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...
The Low-Temperature Radiant Floor Heating System Design and Experimental Stud...
 
Experimental study on critical closing pressure of mudstone fractured reservoirs
Experimental study on critical closing pressure of mudstone fractured reservoirsExperimental study on critical closing pressure of mudstone fractured reservoirs
Experimental study on critical closing pressure of mudstone fractured reservoirs
 
Correlation Analysis of Tool Wear and Cutting Sound Signal
Correlation Analysis of Tool Wear and Cutting Sound SignalCorrelation Analysis of Tool Wear and Cutting Sound Signal
Correlation Analysis of Tool Wear and Cutting Sound Signal
 
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...
Reduce Resources for Privacy in Mobile Cloud Computing Using Blowfish and DSA...
 
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...
Resistance of Dryland Rice to Stem Borer (Scirpophaga incertulas Wlk.) Using ...
 
A novel high-precision curvature-compensated CMOS bandgap reference without u...
A novel high-precision curvature-compensated CMOS bandgap reference without u...A novel high-precision curvature-compensated CMOS bandgap reference without u...
A novel high-precision curvature-compensated CMOS bandgap reference without u...
 
Structural aspect on carbon dioxide capture in nanotubes
Structural aspect on carbon dioxide capture in nanotubesStructural aspect on carbon dioxide capture in nanotubes
Structural aspect on carbon dioxide capture in nanotubes
 
Thesummaryabout fuzzy control parameters selected based on brake driver inten...
Thesummaryabout fuzzy control parameters selected based on brake driver inten...Thesummaryabout fuzzy control parameters selected based on brake driver inten...
Thesummaryabout fuzzy control parameters selected based on brake driver inten...
 

Recently uploaded

2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Hiroshi SHIBATA
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfLoriGlavin3
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...AliaaTarek5
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterMydbops
 
Visualising and forecasting stocks using Dash
Visualising and forecasting stocks using DashVisualising and forecasting stocks using Dash
Visualising and forecasting stocks using Dashnarutouzumaki53779
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Strongerpanagenda
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsNathaniel Shimoni
 

Recently uploaded (20)

2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024Long journey of Ruby standard library at RubyConf AU 2024
Long journey of Ruby standard library at RubyConf AU 2024
 
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdfMoving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL Router
 
Visualising and forecasting stocks using Dash
Visualising and forecasting stocks using DashVisualising and forecasting stocks using Dash
Visualising and forecasting stocks using Dash
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better StrongerModern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
Modern Roaming for Notes and Nomad – Cheaper Faster Better Stronger
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Time Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directionsTime Series Foundation Models - current state and future directions
Time Series Foundation Models - current state and future directions
 

F124144

  • 1. International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 1 Issue 2 ǁ June 2013 ǁ PP.41-44 www.ijres.org 41 | Page A New Approach for Vehicle Detection in Aerial Surveillances Divya Michael1 , Paul P Mathai2 1 (Dept. of Computer Science & Engineering, FISAT Angamaly, M. G. University, India) 2 (Dept. of Computer Science & Engineering, FISAT Angamaly, M. G. University, India) Abstract— Target object detection in aerial surveillance using image processing techniques is growing more and more important. Aerial surveillance is more suitable for monitoring fast-moving targets and covers a much larger spatial area. Aerial view has the advantage of providing a better perspective of the area being covered. These techniques make use of the aerial videos taken from aerial vehicles. Vehicle detection technique has a variety of applications, such as traffic management, police and military. The purpose of this technical report is to provide a survey of research related to the application of vehicle detection techniques for traffic management and other applications and also to present the proposed system. Index Terms—Aerial surveillance, Background subtraction, Feature extraction, Gaussian mixture, SVM, Vehicle classification, Vehicle detection. I. INTRODUCTION In recent years, the analysis of aerial videos taken from aerial vehicle has become an important issue. The increase in the number of vehicles on roadway networks has led transport management agencies to allow use of technology advances resulting in better decisions. According to this perspective aerial surveillance has better place nowadays. Aerial surveillance is more suitable for providing increased monitoring results in case of fast-moving targets and covers a much larger spatial area. So these aerial surveillance systems become excellent supplements of ground-plane surveillance systems. One of the main topics in intelligent aerial surveillance is vehicle detection and tracking. The difficulties involved in the aerial Surveillance include the camera motions such as rotation, panning and tilting. Also the different camera heights largely affect the detection results. Vision based techniques is one of the most common approach to analyze vehicles from images or videos. In this system, design a pixelwise classification method for vehicle detection and novelty lies in the fact that, instead of performing pixelwise classification, relationship among the neighboring pixels in a particular region are preserved in the feature extraction process. The vehicle detection technique consist of three parts i.e., a background subtraction model, vehicle detection and tracking. In this system, design a pixelwise classification method for vehicle detection and novelty lies in the fact that, instead of performing pixelwise classification, relationship among the neighboring pixels in a particular region are preserved in the feature extraction process. The vehicle detection technique consist of three parts i.e., a background subtraction model, vehicle detection and tracking. The Gaussian mixture algorithm used for background subtraction developed by Stauffer and Grimson [1] aims to segment moving foreground objects from relatively stationary objects. Recently pixel-based probabilistic model methods gained lots of interests and have shown good detection results. The values of a particular pixel are modeled as a mixture of adaptive Gaussians. At each iteration Gaussians are evaluated using a simple heuristic to determine which ones are mostly likely to correspond to the background pixels. Those pixels that do not match with the background Gaussians are classified as foreground. In the detection phase, first perform background color removal. Afterward, the feature extraction procedure is performed as in the training phase. The extracted features from feature extraction phase serve as the input to support vector machine, which indicates whether a pixel belongs to a vehicle or not. There is no need to generate multiscale sliding windows either. The detection task is based on pixel wise classification and the features are extracted in a neighborhood region of each pixel. Finally, the extracted features comprise of pixel- level information and also the relationship among neighboring pixels in a region. This design is more effective and efficient than region-based or multiscale sliding window detection methods. This system considers features including local feature points, vehicle colors and edges. For vehicle color extraction, system utilizes color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, system applies moment-preserving method to adjust the thresholds for canny edge detector automatically, which in turn increases the adaptability and accuracy of the system. A support vector machine is
  • 2. A New Approach for Vehicle Detection in Aerial Surveillances www.ijres.org 42 | Page constructed for classification purpose and features are extracted. The extracted features comprise not only pixel- level information but also relationship among neighboring pixels in a region is considered. This paper mentions various methodologies for target object detection in aerial surveillance Section 2 summarizes important works related to vehicle detection and also presented the proposed system and section 3 concludes the discussion. II. RELATED WORK S. Srinivasan, H. Latchman, J. Shea, T. Wong, and J. McNair [2] have introduced a method for the airborne video registration and traffic-flow parameter estimation which focus on airborne helicopter video for estimating traffic parameters. The airborne video is taken from a digital video camera attached to the skid of the helicopter. Roll, pitch, and yaw of the helicopter make the video difficult to view, unstable and the derived parameters less accurate. To avoid this, a frame-by-frame video-registration technique using a feature tracker to automatically determine control-point correspondences is present in the system. This feature tracker is used to track fixed features through the sequence of images in the video. These feature-location correspondences are used as control points to compute a polynomial transformation function to warp every frame in the sequence successively to the reference. The reference frame can be any one of the frames in the video segment. By changing the tracking-window orientation, the same feature tracker can be used to track moving vehicles within the registered video segment. The advantages of the system include excessive reliance on sensor data, inadequate stabilization and availability of elevation maps. S. Hinz and A. Baumgartner [3] utilized a hierarchical model that describes different levels of details of vehicle features. In this system, vehicles are represented in different levels and details of vehicle are increasing from left to right. Hierarchical model describes the appearance of vehicle at different levels. At lowest level vehicle substructures are described like type of vehicle and last level represents 3D information and local context of vehicle. The advantage of this system is that it neither relies on external information like digital map or site models, nor it is limited to any specific vehicle models. As disadvantage, the system would miss vehicles when the contrast is weak or when the influences of neighboring objects are present. H. Cheng and J.Wus [4] have introduced an adaptive ROI estimation algorithm by analyzing the way the camera is operated. The system proposes an ego-motion of the camera computed from adjacent video frames, and deriving the ROI defined by the operator while capturing the video using an adaptive operator attention model. When monitoring a building beside a highway, only those moving objects associated with the building, such as objects entering or leaving the building, are of interest. Therefore, accurate ROI estimation for aerial surveillance applications must be adaptive and cannot always use the same type of objects, such as moving objects as ROI. Lin et al [5] proposed a method by subtracting background colors of each frame and then refined vehicle candidate regions by enforcing size constraints of vehicles. The disadvantage of the system is that, it assumed too many parameters such as the smallest and largest sizes of vehicles, height and the focus of the airborne camera etc. Assuming these parameters as known priors might not be realistic in real applications. Also the vehicle detection method is based on cascade classifiers. Cheng and Butler [6] considered multiple clues and used a mixture of experts to merge the clues for vehicle detection in aerial images. The system consists of color segmentation via mean-shift algorithm and motion analysis via change detection. Also they presented a trainable sequential maximum a posterior method for multiscale analysis and enforcement of contextual information. The system contains a method by subtracting background colors of each frame and then refined vehicle candidate regions by enforcing size constraints of vehicles. A large number of positive and negative training samples need to be collected for the training purpose. Also considers multiscale sliding windows which are generated at the detection stage. The main demerit of this method is that there are a lot of miss detections on rotated vehicles. The faces with poses are easily missed if only frontal faces are trained. Also if faces with poses are added as positive samples, then the number of false alarms would surge. J. Y. Choi and Y. K. Yang [7] have proposed a vehicle detection algorithm using the symmetric property of car shapes. But this is prone to false detections such as symmetrical details of buildings or road markings. In order to avoid this, a log-polar histogram shape descriptor is used to verify the shape of the candidates. The shape descriptor is obtained from a fixed vehicle model, making the algorithm inflexible. Also the algorithm relied on mean-shift clustering algorithm for image color segmentation. Moreover, nearby vehicles might be clustered as one region if they have similar colors. The advantage is that, using symmetric property of car shapes, object can easily detect. Disadvantages include a vehicle tends to be separated as many regions since car roofs and windshields usually have different colors. Also the high computational complexity of mean-shift segmentation algorithm is another concern.
  • 3. A New Approach for Vehicle Detection in Aerial Surveillances www.ijres.org 43 | Page Luo-weitsai, Jun-weihsieh, Kuo-chin fan [8] proposed a novel vehicle detection method using color transform model. The detection procedure consists of different stages. In the first stage a color transformation model is used to separate vehicle colors from nonvehicle colors effectively. This color model transforms (R,G,B) color components into the color domain (u,v). The technique which is adopted for this is dimensionality reduction. Beginning of the technique follows the collection of several thousands of training images and these training images are collected by projecting all colors of input pixels on the color space. The classification is carried out by means of a Bayesian classifier. By using this method the authors insist that vehicles can be very robustly and accurately verified and detected from static images. B. Morris and M. Trivedi [9] proposed a tracking system with ability to classify vehicles into three classes such as sedan, semi, truck+SUV+van. This system was developed after comparing classification schemes using vehicle images and measurements. The most accurate among this learned classifier was integrated into tracking software which greatly improved the accuracy on low resolution traffic video. J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu [10] proposed a vehicle tracking and classification technique to estimate important traffic parameters from video sequences using one camera. The system has the ability to categorize vehicles into more specific classes by introducing a new linearity feature in vehicle representation. Also this system can easily tackle the problem of vehicle occlusion caused by shadows. This problem is solved by shadow algorithm that uses a set of lines to eliminate all unwanted shadows. Background subtraction is a process which aims to segment moving foreground objects from a relatively stationary background. Recently pixel-based probabilistic model methods gained lots of interests and have shown good detection results. Background modeling by Gaussian mixtures is a pixel based process. Let x be a random process representing the value of a given pixel in time. A convenient framework to model the probability density function of x is the parametric Gaussian mixture model where the density is composed of a sum of Gaussians. Let p(x) denotes the probability density function of a Gaussian mixture comprising K component densities. The mixture of Gaussians algorithm, proposed by Stauffer and Grimson [1], estimates these parameters over time to obtain a robust representation of the background.Here every pixel is checked against K Gaussian distributions until a match is found and if no match is found for a pixel, the least probable distribution is replaced with a distribution with current value as its mean value. The advantage includes its good accuracy and disadvantages include high computational cost and high execution time. Zivkovic [11] proposed an approach which reduces the computation time and memory bandwidth. Here, a Bayesian approach is formulated to select the required number of Gaussian modes for each pixel in the scene. In scenes with static background, this approach assigns a single mode Gaussian to model most of the pixels which helps to reduce average processing time by 32%.However in the outdoor video (trees sequence), results show only a 2% improvement since a significantly large portion of the scene requires a multimodal model. In the GMM algorithm the weights of the Gaussian mixture represent the fraction of the data samples ‗x(t)‘ that belongs to the particular mode in the model. This system used a Dirichlet prior with negative coefficients. This is done with an intention of accepting a class only if there is enough evidence from the data samples for the existence of the class. Advantages include fast compared to GMM, efficient and accurate. Disadvantage includes reduced computation time. D.S. Lee [12] proposed an effective gaussian mixture learning for video background subtraction, each pixel in a frame is modeled as a stochastic process and the modes of the GMM are arranged in decreasing order of their weights. A predefined fraction of the weights proposed to use modes with low weights to model the foreground and higher weight to be selected as the background. A match of an incoming pixel to any of the modes defines the pixel as background or foreground. Advantages are fast compared to GMM and makes fast learning. Disadvantages include high memory requirement P. Gorur and B. Amrutur [13] proposed a speeded up gaussian mixture model algorithm for background subtraction combines fast learning of effective GMM with automatic selection of number of modes in improved GMM to obtain a high efficient and accurate scheme. This concept is a modification to his adaptive GMM algorithm that further reduces execution time by replacing expensive floating point computations with low cost integer operations. In order to maintain accuracy, derive a heuristic that computes periodic floating point updates for the GMM weight parameter using the value of an integer counter. Limitations of GMM includes good accuracy of GMM comes at high computational cost, slow learning rate and GMM with more modes is slower compared to single gaussian scheme. Advantages of speeded up GMM include computation time reduction, fast learning and highly efficient and accurate. A. Proposed Method The proposed system presents a new vehicle detection framework that preserves the advantages of the existing works and avoids their drawbacks. The vehicle detection frame work can be classified into training phase and the detection phase [17]. In the training phase, it is possible to extract multiple features including
  • 4. A New Approach for Vehicle Detection in Aerial Surveillances www.ijres.org 44 | Page local edge and corner features. In the detection phase, perform background color removal using speeded up GMM. This speeded up gaussian mixture model algorithm for background subtraction combines fast learning of effective GMM with automatic selection of number of modes in improved GMM to obtain a high efficient and accurate scheme. Then features are extracted in the training phase. The proposed system consist of a new color model to separate vehicle colors from non-vehicle colors effectively and this color model transforms (R,G,B) color components into the color domain (u,v). In this (u,v) color model vehicle colors and nonvehicle colors have less overlapping regions .The support vector machine (SVM) is used to classify vehicle colors and non- vehicle colors effectively. A support vector machine is again used for classification of vehicle and non-vehicle pixel. III. CONCLUSION This paper has focused on important methods to detect vehicles from different video sequences. This vehicle detection system escapes from the existing frameworks of vehicle detection in aerial surveillance, which is either region based or sliding window based. Instead a pixel wise classification method for vehicle detection is proposed. This method checks the relations among neighboring pixels in a region which are preserved in the feature extraction process. Features extracted comprise not only pixel-level information but also region-level information. Finally classify vehicle colors and non-vehicle colors effectively using support vector machine. REFERENCES [1] C. Stauffer and W. Grimson. ―Adaptive background mixture models for real-time tracking.‖ IEEE Conf. on Computer Vision and Patteren Recognition, vol. 2,pp. 246-252, June 1999. [2] S. Srinivasan, H. Latchman, J. Shea, T. Wong, and J.Mc Nair, ―Airborne traffic surveillance systems: Video surveillance of highway traffic,‖ in Pro.ACm 2nd Int. Workshop Video Surveillance Sens.Netw., 2004,pp. 131-135 [3] S. S. Hinz and A. Baumgartner, ―Vehicle detection in aerial images using generic features, grouping, and context,‖ in Proc. DAGM-Symp., Sep.2001, vol. 2191, Lecture Notes in Computer Science, pp. 45–52. [4] H. Cheng and J.Wus, ―Adaptive region of interest estimation for aerial surveillance video,‖ in Proc. IEEE Int. Conf. Image Process., 2005, vol. 3, pp. 860–863. [5] R. Lin, X. Cao, Y. Xu, C.Wu, and H. Qiao, ―Airborne moving vehicle detection for urban traffic surveillance,‖ in Proc. 11th Int. IEEE Conf. Intell. Transp. Syst., Oct. 2008, pp. 163–167 [6] H. Cheng and D. Butler, ―Segmentation of aerial surveillance video using a mixture of experts,‖ in Proc. IEEE Digit. Imaging Comput.—Tech. Appl., 2005, p. 66. [7] J. Y. Choi and Y. K. Yang, ―Vehicle detection from aerial images using local shape information,‖ Adv. Image Video Technol., vol. 5414, Lecture Notes in Computer Science, pp. 227–236, Jan. 2009. [8] L. W. Tsai, J. W. Hsieh, and K. C. Fan, ―Vehicle detection using normalized color and edge map,‖ IEEE Trans. Image Process., vol. 16, no.3, pp. 850–864, Mar. 2007. [9] B. Morris and M. Trivedi, ―Robust classification and tracking of vehicles in traffic video streams,‖ in Proc. IEEE ITSC, 2006, pp. 1078–1083. [10] J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu, ―Automatic traffic surveillance system for vehicle tracking and classification,‖ IEEE Trans.Intell. Transp. Syst., vol. 7, no. 2, pp. 175–187, Jun. 2006. [11] Z. Zivkovic.‖ Improved adaptive gaussian mixture model for background subtraction.‖Int‘l Conf. on Pattern Recognition, 2:28–31, 2004. [12] D.-S. Lee. ―Effective gaussian mixture learning for video background subtraction.‖ IEEE Trans. on Pattern Analysis and Ma-chine Intelligence, 27:827–832, 2005. [13] P. Gorur and B. Amrutur. Speeded up gaussian mixture model algorithm for background subtraction. In IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), pages 386–391, Sept. 2011. [14] C. G. Harris and M. J. Stephens, ―A combined corner and edge detector,‖inProc. 4th Alvey Vis. Conf., 1988, pp. 147–151. [15] J. F. Canny, ―A computational approach to edge detection,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698,Nov. 1986. [16] W. H. Tsai, ―Moment-preserving thresholding: A new approach,‖Comput. Vis.Graph., Image Process., vol. 29, no. 3, pp. 377–393, 1985. [17] Hsu-Yung Cheng, Chih-Chia Weng, and Yi-Ying Chen ―Vehicle Detection in Aerial urveillance Using Dynamic Bayesian Networks‖ IEEE Trans. On Image Processing, Vol. 21, No. 4, April 2012.