Multi Object Tracking | Final Defense | ID 103001

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Slide for Multi Object Tracking by Md. Minhazul Haque, Rajshahi University of Engineering and Technology * Objectives * Object Tracking * Applications * Methodology * Implementation * Experiment Result * Performance Analysis * Future Work * References

Technology

Outlines
❏Objectives
❏Object Tracking
❏Applications
❏Methodology
❏Implementation
❏Experiment Result
❏Performance Analysis
❏Future Work
❏References
2Multi-Object TrackingJanuary 03, 2016

Objectives
❏Study Background Subtraction methods
❏Study Blob Detection methods
❏Study Blob Tracking methods
❏Create a Multi Tracker algorithm
❏Create a simulator to test performance
3Multi-Object TrackingJanuary 03, 2016

Object Tracking
❏Object or Blob is a group of connected
pixels in an image
❏Tracking involves detecting and recognizing
blobs
4Multi-Object TrackingJanuary 03, 2016
Figure 1: A Ball Object
[Courtesy: lirtex.com]
Figure 2: A Car Object
[Courtesy: 4freephotos.com]

Applications of Object Tracking
❏Surveillance in parks or house
❏Track parked vehicles
❏Biomedical analysis
❏Counters in highways/tolls
❏Human Computer Interaction
5Multi-Object TrackingJanuary 03, 2016

Methodology
A system that contains ...
❏Background Subtraction method
❏Noise removal method
❏Blob detection method
❏Blob tracker method
6Multi-Object TrackingJanuary 03, 2016

Methodology [2]
Start
Initialize source
media
Apply BGS
Apply Contour
Detection
Get Object List
Track Objects
Update Objects
Delete Objects
Add Objects
Stream
of frames
Get a frame
Loop until end of
media/frame
7Multi-Object TrackingJanuary 03, 2016

Implementation
8Multi-Object TrackingJanuary 03, 2016
Background Subtraction using Mixture of
Gaussian 2 (MOG2) [1] algorithm
Figure 3: Original Video
Frame
Figure 4: Frame after
Background Segmentation

Implementation [2]
9Multi-Object TrackingJanuary 03, 2016
Removal of Noise using 2D Filtering
(Median Blur), then Thresholding (Binary)
Figure 5: Filtered Frame
(Blur)
Figure 6: Frame after applying
Binary Threshold

Implementation [3]
10Multi-Object TrackingJanuary 03, 2016
❏ Contour detection using Connected Component
retrieval mode
❏ Fit points into Convex Hull
Figure 7: Contour Detection
Figure 8: Fit Contours into
Convex Hull

Implementation [4]
11Multi-Object Tracking
❏ Add/Delete/Update Blobs using properties:
Bhattacharyya Distance [2], Earth Mover Distance[3],
Area, and Contours
January 03, 2016
Figure 9: Blob Matching

Implementation [5]
12Multi-Object Tracking
❏ Label Blobs according to their own ID
January 03, 2016
Figure 10: Labeling Blobs by their ID

Implementation [6]
Technical Information
❏ Language: C++
❏ Framework: OpenCV 3.1.0
❏ IDE: Qt Creator
❏ Platform: Ubuntu 14.04 LTS
Test Data Resources
❏ ViSOR (Video Surveillance Online Repository)[4]
❏ POND5 (Stock Videos of Traffic and Highways)[5]
❏ HSCC1000 Highway Surveillance Videos[6]
13Multi-Object TrackingJanuary 03, 2016

Experiment Result
14Multi-Object TrackingJanuary 03, 2016

Experiment Result [2]
15Multi-Object TrackingJanuary 03, 2016

Experiment Result [3]
Table for Multi Object Tracker Simulation Result
16Multi-Object TrackingJanuary 03, 2016
Videos Duration (mm:ss) Actual Count Tracker Count Error Count
Cars_1.mp4 01:33 36 43 7
Cars_2.mp4 00:35 26 27 1
Cars_3.mp4 00:54 18 20 2
Highway_1.mp4 00:14 5 5 0
Highway_2.mp4 00:10 11 15 4
Highway_3.mp4 01:00 88 98 10
Moon.mp4 00:15 3 3 0
Toll_Counter.mp4 00:27 54 54 0

Performance Analysis
❏Error Rate
❏09.77% based on 10 input videos
❏Processing Time
❏ Max. processing time: 0.031 seconds/frame
❏ Min. processing time: 0.019 seconds/frame
❏Maximum frame rate supported
❏ 32 Hz (1/0.031)
17Multi-Object TrackingJanuary 03, 2016

Future Work
❏Improve Background Subtraction
❏Feature based blob detection
❏Handle occlusion
18Multi-Object TrackingJanuary 03, 2016

References
[1] Zoran Zivkovic. Improved adaptive gaussian mixture model for background
subtraction. 2004.
[2] https://en.wikipedia.org/wiki/Bhattacharyya_distance
[3] http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/RUBNER/
emd.htm
[4] http://imagelab.ing.unimore.it/visor/
[5] http: //www.pond5.com/index.php
[6] https://www.youtube.com/user/HSCC1000
19Multi-Object TrackingJanuary 03, 2016 19

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Presentation of the OECD Artificial Intelligence Review of Germany

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June Patch Tuesday

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Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

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Multi Object Tracking | Final Defense | ID 103001

1. Multi-Object Tracking Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Presented by Md. Minhazul Haque Roll # 103001 Dept. of CSE RUET Supervised by Dr. Md. Ali Hossain Lecturer Dept. of CSE RUET January 03, 2016 Heaven's Light is Our Guide

2. Outlines ❏Objectives ❏Object Tracking ❏Applications ❏Methodology ❏Implementation ❏Experiment Result ❏Performance Analysis ❏Future Work ❏References 2Multi-Object TrackingJanuary 03, 2016

3. Objectives ❏Study Background Subtraction methods ❏Study Blob Detection methods ❏Study Blob Tracking methods ❏Create a Multi Tracker algorithm ❏Create a simulator to test performance 3Multi-Object TrackingJanuary 03, 2016

4. Object Tracking ❏Object or Blob is a group of connected pixels in an image ❏Tracking involves detecting and recognizing blobs 4Multi-Object TrackingJanuary 03, 2016 Figure 1: A Ball Object [Courtesy: lirtex.com] Figure 2: A Car Object [Courtesy: 4freephotos.com]

5. Applications of Object Tracking ❏Surveillance in parks or house ❏Track parked vehicles ❏Biomedical analysis ❏Counters in highways/tolls ❏Human Computer Interaction 5Multi-Object TrackingJanuary 03, 2016

6. Methodology A system that contains ... ❏Background Subtraction method ❏Noise removal method ❏Blob detection method ❏Blob tracker method 6Multi-Object TrackingJanuary 03, 2016

7. Methodology [2] Start Initialize source media Apply BGS Apply Contour Detection Get Object List Track Objects Update Objects Delete Objects Add Objects Stream of frames Get a frame Loop until end of media/frame 7Multi-Object TrackingJanuary 03, 2016

8. Implementation 8Multi-Object TrackingJanuary 03, 2016 Background Subtraction using Mixture of Gaussian 2 (MOG2) [1] algorithm Figure 3: Original Video Frame Figure 4: Frame after Background Segmentation

9. Implementation [2] 9Multi-Object TrackingJanuary 03, 2016 Removal of Noise using 2D Filtering (Median Blur), then Thresholding (Binary) Figure 5: Filtered Frame (Blur) Figure 6: Frame after applying Binary Threshold

10. Implementation [3] 10Multi-Object TrackingJanuary 03, 2016 ❏ Contour detection using Connected Component retrieval mode ❏ Fit points into Convex Hull Figure 7: Contour Detection Figure 8: Fit Contours into Convex Hull

11. Implementation [4] 11Multi-Object Tracking ❏ Add/Delete/Update Blobs using properties: Bhattacharyya Distance [2], Earth Mover Distance[3], Area, and Contours January 03, 2016 Figure 9: Blob Matching

12. Implementation [5] 12Multi-Object Tracking ❏ Label Blobs according to their own ID January 03, 2016 Figure 10: Labeling Blobs by their ID

13. Implementation [6] Technical Information ❏ Language: C++ ❏ Framework: OpenCV 3.1.0 ❏ IDE: Qt Creator ❏ Platform: Ubuntu 14.04 LTS Test Data Resources ❏ ViSOR (Video Surveillance Online Repository)[4] ❏ POND5 (Stock Videos of Traffic and Highways)[5] ❏ HSCC1000 Highway Surveillance Videos[6] 13Multi-Object TrackingJanuary 03, 2016

14. Experiment Result 14Multi-Object TrackingJanuary 03, 2016

15. Experiment Result [2] 15Multi-Object TrackingJanuary 03, 2016

16. Experiment Result [3] Table for Multi Object Tracker Simulation Result 16Multi-Object TrackingJanuary 03, 2016 Videos Duration (mm:ss) Actual Count Tracker Count Error Count Cars_1.mp4 01:33 36 43 7 Cars_2.mp4 00:35 26 27 1 Cars_3.mp4 00:54 18 20 2 Highway_1.mp4 00:14 5 5 0 Highway_2.mp4 00:10 11 15 4 Highway_3.mp4 01:00 88 98 10 Moon.mp4 00:15 3 3 0 Toll_Counter.mp4 00:27 54 54 0

17. Performance Analysis ❏Error Rate ❏09.77% based on 10 input videos ❏Processing Time ❏ Max. processing time: 0.031 seconds/frame ❏ Min. processing time: 0.019 seconds/frame ❏Maximum frame rate supported ❏ 32 Hz (1/0.031) 17Multi-Object TrackingJanuary 03, 2016

18. Future Work ❏Improve Background Subtraction ❏Feature based blob detection ❏Handle occlusion 18Multi-Object TrackingJanuary 03, 2016

19. References [1] Zoran Zivkovic. Improved adaptive gaussian mixture model for background subtraction. 2004. [2] https://en.wikipedia.org/wiki/Bhattacharyya_distance [3] http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/RUBNER/ emd.htm [4] http://imagelab.ing.unimore.it/visor/ [5] http: //www.pond5.com/index.php [6] https://www.youtube.com/user/HSCC1000 19Multi-Object TrackingJanuary 03, 2016 19

20. Thank you! Any question?

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