A Novel Framework for Computing Unique People Count from Monocular Videos

Satarupa Mukherjee, Nilanjan Ray

2014

Abstract

Counting the unique number of people in a video (i.e., counting a person only once while the person is within the field of view), is required in many significant video analytic applications, such as transit passenger and pedestrian volume count in railway stations, malls and road intersections. The principal roadblock in this application is the occlusion. In my PhD thesis, we engineer a novel and straightforward solution to the problem by combining machine learning techniques with simple pixel motion tracking. We estimate the influx and/or the outflux rate of unique people in a region of interest within a monocular video. The unique count is then obtained by summing the influx and/or the outflux rates. Our proposed framework avoids people detection and people tracking that are plagued by occlusions. Also, it is online in nature without error accumulation so that unique people count can be obtained between any two time points in a streaming video. We validate the framework on 19 publicly available monocular 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.

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Paper Citation


in Harvard Style

Mukherjee S. and Ray N. (2014). A Novel Framework for Computing Unique People Count from Monocular Videos . In Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014) ISBN Not Available, pages 3-14


in Bibtex Style

@conference{dcvisigrapp14,
author={Satarupa Mukherjee and Nilanjan Ray},
title={A Novel Framework for Computing Unique People Count from Monocular Videos},
booktitle={Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)},
year={2014},
pages={3-14},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={Not Available},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)
TI - A Novel Framework for Computing Unique People Count from Monocular Videos
SN - Not Available
AU - Mukherjee S.
AU - Ray N.
PY - 2014
SP - 3
EP - 14
DO -