Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities

Afshin Dehghan (), Haroon Idrees (), Amir Roshan Zamir () and Mubarak Shah ()
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Afshin Dehghan: University of Central Florida, Computer Vision Lab
Haroon Idrees: University of Central Florida, Computer Vision Lab
Amir Roshan Zamir: University of Central Florida, Computer Vision Lab
Mubarak Shah: University of Central Florida, Computer Vision Lab

A chapter in Pedestrian and Evacuation Dynamics 2012, 2014, pp 3-19 from Springer

Abstract: Abstract Manual analysis of pedestrians and crowds is often impractical for massive datasets of surveillance videos. Automatic tracking of humans is one of the essential abilities for computerized analysis of such videos. In this keynote paper, we present two state of the art methods for automatic pedestrian tracking in videos with low and high crowd density. For videos with low density, first we detect each person using a part-based human detector. Then, we employ a global data association method based on Generalized Graphs for tracking each individual in the whole video. In videos with high crowd-density, we track individuals using a scene structured force model and crowd flow modeling. Additionally, we present an alternative approach which utilizes contextual information without the need to learn the structure of the scene. Performed evaluations show the presented methods outperform the currently available algorithms on several benchmarks.

Keywords: Human detection; Tracking; Data association; Crowd density; Crowd analysis; Automatic surveillance (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-02447-9_1

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DOI: 10.1007/978-3-319-02447-9_1

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