A Novel Framework for Unique People Count from Monocular Videos Open Access
- Other title
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Ray, Nilanjan (Conputing Sciences)
- Examining committee member and department
Cheng, Irene (Computing Science)
Boulanger, Pierre (Computing Science)
Saha, Punam (Electrical & Computer Engineering, The University of Iowa, USA)
Mandal, Mrinal (Electrical & Computer Engineering)
Zhang, Hong (Computing Science)
Department of Computing Science
- Date accepted
- Graduation date
Doctor of Philosophy
- Degree level
Counting unique number of people in a video (i.e., counting a person only once while the person passes through the field of view (FOV)), is required in many video analytic applications, such as transit passenger and pedestrian volume count in railway stations, malls and road intersections, aid in security and resource management, urban planning, advertising and many others.
In this PhD thesis I have developed a robust algorithm to generate unique people count from monocular videos taken from an arbitrary angle. From applications point of view, my algorithm is one of the most economical ones, because it can work with existing video cameras already mounted. Within a region of interest (ROI) on the FOV of the camera, I compute influx/outflux rate of people, i.e., number of people coming in or going out of the ROI per unit time. Then, I sum the influx/outflux rate between any two time points to estimate the number of people that entered and/or left the ROI within that time interval. I employ two well-known computer vision techniques for this purpose: Gaussian process regression (GPR) to estimate the number of people present within a ROI and optical flow-based tracking of the boundary of the ROI.
The principle roadblock in most of computer vision problems is occlusion. To avoid this bottleneck, we adopt the combination of (a) the concept of influx and outflux of fluid mass from computational fluidics, (b) the GPR to estimate the number of people within a ROI and (c) ROI boundary tracking (as opposed to object or feature tracking) for a short period. Thus, the principal contribution of the thesis is to successfully handle occlusions by computing the average influx and/or outflux of people and avoiding people detection and tracking.
We validate the proposed algorithm on 19 publicly available monocular benchmark videos. Occlusions are abundant in these videos, yet we obtain more than 95% accuracy for most of these videos. We also extend our proposed framework beyond monocular videos and apply it on multiple views of a publicly available dataset with about 99% accuracy.
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
- Citation for previous publication
S. Mukherjee, B. Saha, I. Jamal, R. Leclerc, N. Ray, A novel framework for
automatic passenger counting, IEEE ICIP (2011).S. Mukherjee, N. Ray and S. Acton, Counting cells from microscopy videos
without tracking individual cells, Accepted to be published in IEEE ISBI
(2014).S. Mukherjee and N. Ray, DTV: Detection, Tracking and Validation Framework
for Unique People Count, IJCSNS Vol 2.
No.1 (2014).S.Mukherjee and N. Ray, A Novel Framework for Unique People Count from
Monocular Videos, VISIGRAPP (2014).
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