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2014 | OriginalPaper | Chapter

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

Authors : Afshin Dehghan, Haroon Idrees, Amir Roshan Zamir, Mubarak Shah

Published in: Pedestrian and Evacuation Dynamics 2012

Publisher: Springer International Publishing

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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.

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next chapter London Bridge: The Role Pedestrian Modelling Played in Designing the New Station
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Metadata
Title
Automatic Detection and Tracking of Pedestrians in Videos with Various Crowd Densities
Authors
Afshin Dehghan
Haroon Idrees
Amir Roshan Zamir
Mubarak Shah
Copyright Year
2014
Book
Pedestrian and Evacuation Dynamics 2012

Print ISBN: 978-3-319-02446-2

Electronic ISBN: 978-3-319-02447-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-02447-9_1

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