Multiple human tracking in high-density crowds

doi:10.1016/j.imavis.2012.08.013

2012-12-01

PRE

Irshad Ali *

Matthew N. Dailey

DOI：10.1016/j.imavis.2012.08.013

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摘要

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In this paper, we introduce a fully automatic algorithm to detect and track multiple humans in high-density crowds in the presence of extreme occlusion. Typical approaches such as background modeling and body part-based pedestrian detection fail when most of the scene is in motion and most body parts of most of the pedestrians are occluded. To overcome this problem, we integrate human detection and tracking into a single framework and introduce a confirmation-by-classification method for tracking that associates detections with tracks, tracks humans through occlusions, and eliminates false positive tracks. We use a Viola and Jones AdaBoost detection cascade, a particle filter for tracking, and color histograms for appearance modeling. To further reduce false detections due to dense features and shadows, we introduce a method for estimation and utilization of a 3D head plane that reduces false positives while preserving high detection rates. The algorithm learns the head plane from observations of human heads incrementally, without any a priori extrinsic camera calibration information, and only begins to utilize the head plane once confidence in the parameter estimates is sufficiently high. In an experimental evaluation, we show that confirmation-by-classification and head plane estimation together enable the construction of an excellent pedestrian tracker for dense crowds. (C) 2012 Elsevier B.V. All rights reserved.

Keyword:

Head detection

Pedestrian tracking

Crowd tracking

Particle filters

3D object tracking

3D head plane estimation

Human detection

Least-squares plane estimation

AdaBoost detection cascade