Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
A Small Helping Hand from me to my Engineering collegues and my other friends in need of Object Detection
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
This is a presentation on the Yolo(You Only Look Once) object detection system. This is a state-of-the-art system that is works very fast. The presentation has been derived from the paper cited below
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
A Novel Approach for Moving Object Detection from Dynamic BackgroundIJERA Editor
In computer vision application, moving object detection is the key technology for intelligent video monitoring
system. Performance of an automated visual surveillance system considerably depends on its ability to detect
moving objects in thermodynamic environment. A subsequent action, such as tracking, analyzing the motion or
identifying objects, requires an accurate extraction of the foreground objects, making moving object detection a
crucial part of the system. The aim of this paper is to detect real moving objects from un-stationary background
regions (such as branches and leafs of a tree or a flag waving in the wind), limiting false negatives (objects
pixels that are not detected) as much as possible. In addition, it is assumed that the models of the target objects
and their motion are unknown, so as to achieve maximum application independence (i.e. algorithm works under
the non-prior training).
Efficient and accurate object detection has been an important topic in the advancement of computer vision systems.
Our project aims to detect the object with the goal of achieving high accuracy with a real-time performance.
In this project, we use a completely deep learning based approach to solve the problem of object detection.
The input to the system will be a real time image, and the output will be a bounding box corresponding to all the objects in the image, along with the class of object in each box.
Objective -
Develop a application that detects an object and it can be used for vehicles counting, when the object is a vehicle such as a bicycle or car, it can count how many vehicles have passed from a particular area or road and it can recognize human activity too.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
This is a presentation on the Yolo(You Only Look Once) object detection system. This is a state-of-the-art system that is works very fast. The presentation has been derived from the paper cited below
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}
A Novel Approach for Moving Object Detection from Dynamic BackgroundIJERA Editor
In computer vision application, moving object detection is the key technology for intelligent video monitoring
system. Performance of an automated visual surveillance system considerably depends on its ability to detect
moving objects in thermodynamic environment. A subsequent action, such as tracking, analyzing the motion or
identifying objects, requires an accurate extraction of the foreground objects, making moving object detection a
crucial part of the system. The aim of this paper is to detect real moving objects from un-stationary background
regions (such as branches and leafs of a tree or a flag waving in the wind), limiting false negatives (objects
pixels that are not detected) as much as possible. In addition, it is assumed that the models of the target objects
and their motion are unknown, so as to achieve maximum application independence (i.e. algorithm works under
the non-prior training).
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Development of wearable object detection system & blind stick for visuall...Arkadev Kundu
It is a wearable device. It has a camera, and it detects all living and non living object. This module detects moving object also. It is made with raspberry pi 3, and a camera. One headphone connect with raspberry pi. When this module detects items, it gave a sound output through headphone. Hence the blind man know that item, which is in-front of him or her. We made it in very low budget, and it is very helpful for visually challenged people. And the Blind stick help him to detect obstacles.
Real Time Detection of Moving Object Based on Fpgaiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Real-time Moving Object Detection using SURFiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.
Automated Traffic sign board classification system is one of the key technologies of Intelligent
Transportation Systems (ITS). Traffic Surveillance System is being more and important with improving
urban scale and increasing number of vehicles. This Paper presents an intelligent sign board
classification method based on blob analysis in traffic surveillance. Processing is done by three main
steps: moving object segmentation, blob analysis, and classifying. A Sign board is modelled as a
rectangular patch and classified via blob analysis. By processing the blob of sign boards, the meaningful
features are extracted. Tracking moving targets is achieved by comparing the extracted features with
training data. After classifying the sign boards the system will intimate to user in the form of alarms,
sound waves. The experimental results show that the proposed system can provide real-time and useful
information for traffic surveillance.
Computer m
emory is expensive and the recording of data captured by a webcam needs memory. I
n order to minimize the
memory usage in recording data from human motion as recorded from the webcam, this algorithm will use motion
detection as applied to a process to measure the change in speed or vector of an object in the field of view. This
applicat
ion only works if there is a motion detected and it will automatically save the captured image in its designated
folder.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Detection and Tracking of Moving Object: A SurveyIJERA Editor
Object tracking is the process of locating moving object or multiple objects in sequence of frames. Object
tracking is basically a challenging problem. Difficulties in tracking of an object may arise due to abrupt changes
in environment, motion of object, noise etc. To overcome such problems different tracking algorithms have been
proposed. This paper presents various techniques related to object detection and tracking..The goal of this paper
is to present a survey of these techniques.
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1. Heaven’s Light is Our Guide
Rajshahi University Of Engineering & Technology
Department Of Electronics & Telecommunication Engineering
Moving Object Detection in Video Surveillance
PRESENTED BY
Md. Ashfaqul Haque
Roll: 124006
Md. Sharful Insan
Roll: 124007
Department of Electronics &
Telecommunication Engineering,
RUET
SUPERVISED BY
Prof. Dr. Md. Rabiul Islam
Head,
Department of Computer
Science & Engineering, RUET
1
3. Introduction
Object Detection is a computer
technology related to computer
vision.
Detection is basically locating
object in an image or a video
sequence.
Moving object detection
applications are car detection,
person identification or wild life
monitoring.
It is used in security monitoring,
intelligent perception . Figure[9]: Moving Object Detection
3
4. Motivation
Object detection system introduces a new technology
of security.
It is used different organization, institution, offices and
social sites.
Object Detection is used to control and reduce ,
terrorism, crime, robbery, shop lifting, and accidents.
4
5. Objectives
Develop a computational model to identify the moving
objects by using background subtraction
Detect the moving objects in various scenarios
Develop a comparative result of efficiency for better
object detection
Develop an application for the smart surveillance system
using object detection
5
6. Related Works
A great deal of work have been done on object
detection using various method
An approach is used for moving object detection and
tracking in indoor environment[1].
A Detection method of moving object based on
background subtraction[2].
An efficient real time moving object detection method for
video surveillance[3].
A moving objects detection algorithm based on improved
background subtraction[4].
Motion human detection based on background
Subtraction[5].
6
7. Related Works(cont.)
Moving object detection in spatial domain using
background removal techniques - State-of-Art[6].
A method of detecting and tracking object in wide
area surveillance using Thermal Imagery[7].
A method based on the kernel method is used to
detect and tracking moving object[8].
7
8. Proposed Methodology
In our proposed method we have used Background
Subtraction.
First we have taken frames from the video sequences
and extracted the background frame and the current
frame. Then subtract the background frame from the
current frame.
Then we have used some filtering techniques to
remove noisy areas.
Finally we have used morphological operation on the
subtraction frame and get the final output.
8
9. Flowchart
Input Video
Sequences
Extract Frame
Convert to Gray
Scale
Convert to HSV
Moving Frame &
Background
Frame
Output
Morphological
operation
Boundary
Labelling
Binary Image
Median Filtering
Background
Subtraction
9
10. Workflow
Take a video sequence
From the sequence extract
the background and moving
frame
Figure: Background Frame
Figure : Moving Frame
10
11. Workflow (cont.)
Convert the extracted frame
to the HSV format
H – Hue, S – Saturation,
V - Value
Hue represents the color type
Saturation represents the
vibrancy of the color
Value represents the
brightness of the color
Figure : HSV image
11
12. Workflow(cont.)
Subtract the background frame from the moving frame
and get subtracted image. Here subtraction is
performed using the bitwise XOR
A bitwise XOR takes two bit patterns of equal length
and performs the logical exclusive OR operation on
each pair of corresponding bits
Bitwise XOR operation
0 XOR 0 = 0
0 XOR 1 = 1
1 XOR 0 = 1
1 XOR 1 = 0
12
13. Workflow(cont.)
Then convert image to
gray scale
Figure : Gray scale image
Gray scale image is
converted to the Binary
image
Luminance greater than
level with the value 1
(white) and all other pixels
with the value 0 (black)
Figure : Binary image
13
14. Workflow(cont.)
Here the Binary image is
filtered using median
filtering to remove noises
Figure : Median filtered image
Boundary labeling have
been used to remove the
areas which are less
connected or not
connected
Figure : Boundary labelling
14
15. Workflow(cont.)
Closing is defined simply as
a dilation followed by an
erosion using the same
structuring element for both
operations
Closing operation have
been used to remove the
holes inside the image
objects Figure : Image after closing
15
16. Experimental Results
We have implemented our method for detecting
object using MATLAB software
We have tested our implemented method by the video
captured by camera and sequences collected from
internet. The sequences are captured in different
scenarios
We have calculated the accuracy for our proposed
method and compare with the other existing method
16
19. Experimental Results(cont.)
Accuracy Calculation :
Calculate the true positive(TP), true negative(TN), false
negative(FN) and false positive(FP) value comparing
with ground truth value to measure accuracy
TP : Detected value matched with the truth value
TN : No value both in the detection and ground truth
FN : Rejected in detection but present in the ground
truth
FP : Wrong detection but not present in the ground
truth
19
25. Conclusion
A background subtraction technique for detecting
object has been proposed here
We have got accuracy for the proposed method
94.3204 % which is better than the Kim & Hwang
method and Dewan & Chae method
Moving object detection is always a challenging task
and there is a room to improve this method
25
26. Limitations & Future Work
Our method can not detect the moving target in heavy
rain condition and other natural calamities
It can not detect moving target in long distance video
sequences
We have to reduce this limitations in future
26
27. References
[1] Shucai Wang, Liying Su, Kim Wang and Yueqing Yu, “Moving Object
Detection and Tracking in Indoor Environment”. 2011 International Conference
on Electronic & Mechanical Engineering and Information Technology.
[2] Mr. Mahesh C. Pawaskar1, Mr. N. S.Narkhede2 and Mr. Saurabh S. Athalye1,
“Detection Of Moving Object Based On Background Subtraction” Volume 3,
Issue 3, May-June 2014.
[3] Pranab Kumar Dhar*, Mohammad Ibrahim Khan*, Ashoke Kumar Sen Gupta ,
“An Efficient Real Time Moving Object Detection Method for Video
Surveillance System” International Journal of Signal Processing, Image
Processing and Pattern Recognition. Vol. 5, No. 3, September, 2012
[4] Niu Lianqiang and Nan Jiang, "A moving objects detection algorithm based
on improved background subtraction," Intelligent Systems Design and
Applications, 2008, ISDA '08, Eighth International Conference on Volume 3, 26-
28 Nov. 2008, pp 604 – 607.
[5] Lijing Zhang, Yingli Liang “Motion human detection based on background
Subtraction” 2010 Second International Workshop on Education Technology
and Computer Science, pp 284-287.
27
28. References
[6] Shireen Y. Elhabian, Khaled M. El-Sayed and Sumaya H. Ahmed, “Moving
Object Detection in Spatial Domain using Background Removal Techniques -
State-of-Art” Recent Patents on Computer Science 2008, 1, 32-54.
[7] Santosh Bhusal, “Object Detection and Tracking in Wide Area Surveillance
Using Thermal Imagery”.
[8] Huanhai Yang ,Shandong Institute of Business and Technology, Yantai,
Shandong 264005, China, “Research on the Detection and Tracking of Moving
Target based on Kernel Method” Vol.8, No.2 (2015),pp.91-100.
[9]
https://www.google.com/search?q=moving+object+detection&oq=movi
ng+&aqs=chrome.0.69i59j69i61j69i57j69i61l2j0.3548j0j7&sourceid=chro
me&ie=UTF-8
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