multi object tracking | presentation 2 | id 103001
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A Hybrid Multi-Object Tracking System
Heaven's Light is Our Guide
Rajshahi University of Engineering and TechnologyDepartment of Computer Science and Engineering
Presented byMd. Minhazul HaqueRoll # 103001Dept. of CSERUET
Supervised byMd. Arafat Hossain
Assistant ProfessorDept. of CSE
RUETOctober 13, 2015
Outlines❏Objectives❏Overview of Object Tracking❏Existing Works❏Proposed Methodology❏Implementation❏Experiment Result❏Performance Analysis❏Future Work❏References
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Objectives❏Create a Multi Tracking Method❏Better background subtraction method❏Reduce noise in input videos❏Minimize the error rate
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Object
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
A bird
A car
Image Courtesy: 4freephotos
❏Object is a group of pixels with similar property, also called a blob
❏Anything can be an Object, A ball, car or bird
Object Tracking
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
❏Locate Objects over time❏Save Object List into memory❏Set unique ID to each Object❏Loop until media/input ends
Existing Works❏Tracking and Motion Estimation[1]
❏ Developed by MATLAB❏MOT Challenge 2015[2]
❏ Developed by❏ University of Adelaide Research Centre❏ Swiss Federal Institute of Technology, Zürich❏ TU Darmstadt
❏OpenCV Blob Tracker[3]
❏ Deprecated
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Proposed Methodology
❏ A Hybrid System that contains❏ Background Subtraction Method❏ Noise removal method❏ Blob tracker method
❏Popular Tracker Algorithms❏ Kalman Filter❏ CamShift Algorithm❏ Optical Flow (Not used right now)
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
MOT: Block Diagram
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Start Initialize source media
Apply BGS
Apply Contour DetectionGet Object List
Track Objects
Update Objects
Delete Objects
Add Objects
Streamof frames
Get a frame
Loop untilend of media/frame
Implementation❏Language: C++❏Framework: OpenCV 2.4.10❏IDE: Qt Creator❏Platform: Ubuntu 14.04 LTS
Data Resources❏MOT Challenge Website[2]
❏HSCC1000 Highway Surveillance Videos[4]
❏RUET Area Videos (Noisy)
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Initialize Source Media❏Loaded video files using OpenCV
VideoCapture class
Apply BGS Method❏Tested the following methods
❏ GMM (Gaussian Mixture Model)❏ MOG2 (Mixture of Gaussian version 2)❏ ViBe (Used by American Navy, Closed Source)
Implementation [2] Initialization
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Implementation [3] BGS Comparison
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MOG2Perfect
GMM/MOGNoisy
A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Implementation [4] Noise Removal
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Noise Removal Techniques
❏ Apply medianBlur filter❏ Convert image to Binary
from Grayscale to remove shadow❏Apply erode and dilate technique
Image Courtesy: mimage.me, slice.org
Implementation [5] Extract Blob Info
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❏Apply Canny Edge filter❏Apply Contour detection using findContours❏Fit detected points into a boundingRect❏Store the following blob information into
memory❏ Center❏ Size❏ Angle/Orientation❏ Histogram❏ ID
Image Courtesy: opencv-srf.blogspot.com
Implementation [6] Process Blobs
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❏Match blob features and update properties❏ Match Histogram (CamShift)❏ Position (Kalman Filter)❏ Orientation (Fit Ellipse)❏ Motion/Trajectory
(Use previous position history, not implemented)❏ Screen position (Viewport edges)
❏Assign incremented ID to new objects❏Remove objects that are out of the screen
Implementation [7] Result
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Time 00:00:11.3 Time 00:00:12.8
Experiment Result
Result of Proposed System
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Videos Duration MOT
Highway_Surveillance_1.mp4 01:00 138
Highway_Surveillance_2.mp4 01:00 179
RUET_Area_1.mp4 00:49 20
Talaimari_Area_1.mp4 02:30 69
Performance Analysis
Comparison of Object Tracking Result
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Videos Duration Actual Objects
Kalman Filter CamShift MOT
Highway_Surveillance_1.mp4 01:00 131 83 120 138
Highway_Surveillance_2.mp4 01:00 155 96 106 179
RUET_Area_1.mp4 00:49 24 21 23 20
Talaimari_Area_1.mp4 02:30 59 63 45 69
Performance Analysis: Bar Chart
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
Figure: Bar Chart using the data of multiple tracking methods
Number of Objects Detected
Future Work❏Implement Optical Flow algorithm❏Calculate trajectory❏Create a GUI❏Export data to Excel and CSV format
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References
1.http://www.mathworks.com/help/vision/tracking-and-motion-estimation.html
2.https://motchallenge.net3.https://code.google.com/p/cvblob/4.https://www.youtube.com/user/HSCC1000
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Thank you!
Any question?
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A Hybrid Multi-Object Tracking SystemOctober 13, 2015
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