Object detection is a main role in image processing.The proposed methods detect various multiple object detection using image processing.

1. By- Saurabh Kumar
Reg. No.-15MCA1009
Guided By:
Dr.Priyadarshini.J
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2. . Abstract
. Introduction
. Existing System
. Proposed work
. Module
. Implementation details
. Screen shots
. Software/Tools required
. References
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3. In this paper; the proposed methods
detect various multiple object
detection using image processing.
Object detection is a main role in
image processing. This paper
proposed the use of two methods
Haar Training and Ada Boost
Classifier Algorithm for training for
multiple detection.
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4.  Object detection and tracking - identification of an object
in image and locate .
 Multiple object detection - very difficult topic in computer
vision.
Single object tracking
Multi object tracking
 Tracking detected objects frame by frame in video is a
significant and difficult task.
 Haar Training is main role in image processing for object
detection and object Tracking.
 Object Tracking uses :- motion-based recognition, video
index, etc
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5.  Now a days we are facing various
drawback.
 Object are not perfect aligned in
Training.
 Multiple Kernel Template .
 Identify the color of images.
 Each image using multiple
segmentation.
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6.  The supervised learning - Haar Training the
system with the target for automatic
detection.
 The Haar features used because they
effectively capture different image details,
and it is fast algorithm .
 The Haar Training object detector used to
detect the location of a car in a image.
 First detect the object, then track object by
the use the vision.
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7. 2/19/2017RBL Fall 2015 Review 1I 7
Fig. 1. Process of Object Detection
Object
Training
Object
Detection
Object
Tracking

8. 1.Multiple Object Training:-
 Haar Training is used for multiple object detection in the
image.
 Training the provide the detecting facilities.
2.Multiple Object Detection:-
 The main work multiple image detecting together.
3.Multiple Object Tracking:-
 In this tracking section detecting the image one by one .
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9. Object
Tracking
4
Video
sequence 1
Object
Detected 2
Object
Classification
3
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Fig. 2 Detection Details

10. .
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Video sequence
Object
Detection
Object
Classification
Object Tracking
Frame
Differencing
Background
Subtraction
Shape-based
Color-based
Texture-
based
Point-based
Kernel-based

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Fig. 3 Video Format Details

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Fig. 4 Snapshot picture

13.  MATLAB R2013a
 Windows 8.1
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14. [1] Chensheng Sun and Kin-Man Lam,”Multiple-
Kernel,Multiple-Instance Similarity Features for Efficient
Visual Object Detection” IEEE Transactions on “ vol. no
(22), issue number (8) pp. 3050-3061, 2013.
[2] Asako Kanezaki1, Sho Inaba 1, Yoshitaka Ushiku1,
Yuya Yamashita1, Hiroshi Muraoka1, Yasuo Kuniyoshi1
and Tatsuya Harada1,” Hard Negative Classes for Multiple
Object Detection” “IEEE Transaction on “pp.3066-
3073,2014.
[3] Hongliang Li, Fanman Meng, and King Ngi Ngan,” Co-
Salient Object Detection From Multiple Images ”, “IEEE
Transaction on” vol. no (15), issue number(8) pp. 1896-
1909,2013.
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15. [4]. Srishtee Jain and Surendra Chadokar, “A Object in Image
processing” International
Journal of Electrical, Electronics and
Computer Engineering 4(2): 26-29(2015).
[5] Xin Mao, Feihu Qi, Wenjia Zhu, “Multiple –part based
Pedestrian Detection Using Interfering Object Detection” IEEE
0-7695-2875-9/07 2007.
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16. THANK YOU
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