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

1. Guided By -
Prof. Shivprasad Patil,
Head Of the Dept.
Information Technology,
NBN Sinhgad School of
Engineering.
Made By –
Akshay Gujarathi 23
Vipul Oswal 47
Priya Adwani 53
Kadambari Metri 82

2. Introduction
 The modern world is enclosed with gigantic masses of digital visual information.
 To analyze and understand this huge sea of visual information, there exist many
image analysis techniques.
 Those methods that automatically recognize and detect the objects prove to be of
great use and provide a significant help in modern applications and devices.
 The semantic and syntactic contents of the images and videos can be recognized
and further processed to get the necessary information.The potential uses of the
image can be identified.
 The important content of image is the objects in the image. There exists a
significant and essential need for object recognition techniques.
 Recognition is an important task in image processing and computer vision. A set of
known tags can be used to identify what really the object is and help to extract
information.

3. Motivation And Purpose
 The basic motivation behind this topic is that it is something that will overdo all the
physical tasks.
 Robotics and smart systems are buzzing around all over the world.
 Object recognition and tracking reduces human efforts and provides efficiency.
 It is of interest as it may help humans to be aware of minute information about
particular objects and reduce human tasks.
 Automatic recognition and extraction adds to the smart systems used today.

4. I. Object Representation:
 In a tracking, an object can be defined as anything that is of interest. For example, boats
on the sea, fish inside an aquarium, vehicles on a road, planes in the air.
 People walking on the road are a set of objects that may be important to track in a
specific domain. The appearance and shapes can be represented by object. First we will
describe the representation of object shape.
 Representation of objects is very important in object detection and tracking. There are
various ways used to represent objects.

5.  Points:
The figures to the right show the
use of points in object
representation.
 Primitive Geometric Shapes:
Shapes like rectangles, ellipses
can be used to represent objects.
 Object Silhouette And Contour:
Contour representation defines
the boundary of an object. The
region inside the contour is called
the silhouette of the object.

6.  Articulated Shape models:
Articulated objects are composed of body parts that are held
together with joints. For example, the human body is an articulated
object with torso, legs, hands, head, and feel connected by joints.
 Skeletal Models:
Object skeleton can be extracted by applying medial axis transform
to the object silhouette .This model is commonly used as a shape
representation for recognizing objects.
 Probability Densities Of Object Appearance:
The probability density estimates of the object appearance can either be parametric,
such as Gaussian and a mixture of Gaussians, Parzen windows and histograms.

7. II. Difficulties And Problems In Object
Detection.
 Illumination
The lightning conditions may differ during the course of the day. Also the weather
conditions may affect the lighting in an image
 Positioning
The change in position must not affect the recognition system.
 Rotation
The image can be in rotated form. The system
must be capable to handle such difficulty

8.  Mirroring
The mirrored image of any object must be recognized by the object recognition system.
 Occlusion
The condition when object in an image is not
completely visible is referred as occlusion.
 Scale
Changes in the size must not affect the Occluded car
recognition system

9. III. Techniques for object recognition.
 Template Matching:
Template matching is a technique for finding small parts of an image which match a
template image. It is a straightforward process.

10.  Colour Based:
The object detection using colours involved in the objects is also significantly used and
provide a simple to implement method. It provides potent information for object
recognition. Color histograms prove to be simple and efficient and provide an edge for
the same. The use and importance of color attributes for identifying objects has been
proposed to us by Fahad Khan. This information has been segmented into two
approaches which is the part based approach and the efficient sub-window approach.
Feature combination, photometric invariance and compactness are the three major
features that need to be taken into account while integrating or appending the color
attributes with the object detection.
 Shape Based:
Lately, shape has proved to be of great importance in object recognition. They have
been explored dramatically to recognize objects in real world acquainted images. These
features also provide an upper hand over local features like SIFT as most of the objects
are illustrated and described by their shapes and textures such as different animals and
other varying objects. They are most likely used to add an advantage to the local
features.

11. IV. Extraction Of Object
 Background Subtraction:
The background subtraction method is the common method of motion detection. It is
a technology that uses the difference of the current image and the background image
to detect the motion region, and is generally able to provide data included in object
information. The background image is subtracted from the current frame. If the pixel
difference is greater than the set threshold value T, then it determines that the pixels
from the moving object, otherwise, as the background pixels.

12. Background Subtraction
The result of image sequences computed by the method here is in the following
figures.
• When there is no movement in the frames.
When there is no movement in the image sequences then the difference between
the two images shows a black binary output image shows there is no difference in a
single pixel.
(a) Input first frame (b) Input second frame
Binary image of difference image.Difference between two frames

13. • When there is movement in the frame.
When there is movement in the scenes then the binary image of the
difference between the two frames shows motion having white colour and
where there is no change shows black colour.
(a) Input first frame (b) Input second frame
Binary image of difference image.Difference between two frames
showing moving object

14. V. Multiple Object Detection & Single
Object Detection
 Multiple Object Detection:
An image may consist of many objects. It may comprise
of a single object or they may be many. There exist different
methods for the recognition of the same and these methods
are independent of each other. The flowchart to the right
depicts a simple overview of how the multiple objects can be
detected.
It involves a very simple procedure of training the detectors
and then these detectors are used for identifying the objects
either by extracting the features or the boundaries of the
objects. These detectors need to be already trained for the
different objects that exist and they work in efficient way to
serve the purpose. An input image is tested against the
detectors and compared and finally the output that is the
final objects that are detected are displayed.
Input ImageInput Image
Object 1
Detector
Object 2
Detector
Object 3
Detector
Training
with Object
1
Training
with
Object 2
Training
with
Object 3
Output Image with all
Objects detected.

15.  Stationary Object Detection
The flowchart provides an
efficient and simple to
implement procedure for
stationary object detection.
This process is simple and
straight forward. The input
is firstly segmented and then
classified as either multiple or
stationary using the time parameter
and the other mathematical analysis.
N Y
Video Sequence
Absolute
Differencing
Threshold
Segmentation
Multiple Object
Tracking
Background
Modelling using
Median
Running Average
Background
updation
If any Object
Stationary for
1 second?
Declared as
Stationary

16. VI. Machine Learning Process In Object
Detection.
 Each of the methods that have
been reviewed and analyzed require
machine learning to be an integral
part of it since no matter what the Trained
image is, the detectors have to be
trained for the objects to be
recognized and to do this the
machine needs to be trained.
 So, this brings up the concept about
Artificial Intelligence in terms of object
recognition. The detectors basically
keep on building their database by
feature extraction or other attributes like
color, shape and then these features
are used to match with the objects
in the input image.
Input
image
Detected
Object
Detectors

17. VII. Object Tracking
 Object Tracking is a major phase involved in many remote sensing applications.
Object tracking is to track an object (or multiple objects) over a sequence of
images. It can be defined as a process of segmenting an object of interest from a
video scene and keeping track of its motion, occlusion, orientation etc in order to
extract the useful information.
 Point Tracking:
Objects detected in consecutive frames are represented by points.
 Kernel Tracking:
Kernel tracking is usually performed by locating the moving object, which is
represented by an embryonic object region, from one frame to the next.
 Silhouette Tracking:
In this approach Silhouette is extracted from detected object. Silhouette tracking
methods make use of the information stored inside the object region.

18. VIII. Applications
1. Biometric recognition
2. Surveillance
3. Industrial inspection
4. Content-based image retrieval (CBIR)
5. Robotics
6. Medical analysis
7. Lane Detection

19. Introduction to Lane Detection
 What is Lane Detection?
Technically Lane detection is defined, as a well-researched area of computer vision
with applications in autonomous vehicles and driver support systems.
The lane detection task involves understanding the topology of the lanes around the
car.

20. Lane Detection System(LDS)
Block Diagram
Indicating the
result by means
of visuals or
audio
Detection of
lane
Input image
from camera

21. Lane Detection Implementation
Input image Selected lane and position Indicator on screen
1. A novel technique is used to recognize lane for a various road and illumination,
lane markings conditions such as damaged road surfaces blocked by a car,
shadow, backlights, etc.
2. The basic transform used will be HOUGH transform along with the
segmentation of image concept to detect the lanes without any errors or flaws.

22. Lane Detection System Flow
& Pseudocode

23. Overview

24. Applications
 Vehicle Driver Assistance Systems.
 Automated Surveillance.
 Military Applications.
 Security.

25. IX. Conclusion
In this presentation, we have overviewed the following points –
1. Basic concept of Object Detection and Tracking.
2. Problems and difficulties in Object Recognition.
3. Representation of objects.
4. Techniques in object recognition.
5. Multiple and single object detection and machine learning process.
6. Object tracking.
7. Applications.
Thus we conclude –
• Object detection is a task of extracting Objects from specific frames/images.
• Object detection is one of the most widely used concept in the field of
Artificial Intelligence.
• Has a great scope in future for the development of the modern world.

26. References & Bibliography
 Himani Parekh , Darshan Thakore , Udesang Jaliya,
“A Survey On Object Detection And Tracking Methods”,
International Journal of Innovative Research in Computer and Communication Engineering,
February 2014.
 Kinjal Joshi , Darshak Thakore,
“A Survey Of Moving Object Detection And Tracking In Video Surveillance Systems”,
International Journal of Soft Computing And Engineering, July 2012.
 Sukriti Srivastava, Ritika Singal, Manisha Lumb,
“Efficient Lane Detection Algorithm using Different Filtering Techniques”,
International Journal of Computer Applications, February 2014.

27. Any Questions
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28. Developed By-
Mr.Akshay Gujarathi
(Designing Head)
Contact - 922 6666 86