Multi Object Tracking | Final Defense | ID 103001

Multi-Object Tracking

Rajshahi University of Engineering and TechnologyDepartment of Computer Science and Engineering

Presented byMd. Minhazul HaqueRoll # 103001Dept. of CSERUET

Supervised byDr. Md. Ali Hossain

LecturerDept. of CSE

RUETJanuary 03, 2016

Heaven's Light is Our Guide

Outlines

❏Objectives❏Object Tracking❏Applications❏Methodology❏Implementation❏Experiment Result❏Performance Analysis❏Future Work❏References

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Objectives

❏Study Background Subtraction methods❏Study Blob Detection methods❏Study Blob Tracking methods❏Create a Multi Tracker algorithm❏Create a simulator to test performance


Object Tracking

❏Object or Blob is a group of connected pixels in an image

❏Tracking involves detecting and recognizing blobs


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


Methodology

A system that contains ...❏Background Subtraction method❏Noise removal method❏Blob detection method❏Blob tracker method


Methodology [2]

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 until end of media/frame


Implementation


Background Subtraction using Mixture of Gaussian 2 (MOG2) [1] algorithm

Figure 3: Original Video Frame

Figure 4: Frame after Background Segmentation

Implementation [2]


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]


❏ 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]

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❏ 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]

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❏ 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 LTSTest Data Resources❏ ViSOR (Video Surveillance Online Repository)[4]

❏ POND5 (Stock Videos of Traffic and Highways)[5]

❏ HSCC1000 Highway Surveillance Videos[6]


Experiment Result


Experiment Result [2]


Experiment Result [3]Table for Multi Object Tracker Simulation Result


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)


Future Work

❏Improve Background Subtraction❏Feature based blob detection❏Handle occlusion


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

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Thank you!Any question?