Multiple Human Tracking in High-Density Crowds

摘要

In this paper, we present a fully automatic approach to multiple human detection and tracking in high density crowds in the presence of extreme occlusion. Human detection and tracking in high density crowds is an unsolved problem. Standard preprocessing techniques such as background modeling fail when most of the scene is in motion. We integrate human detection and tracking into a single framework, and introduce a confirmation-by-classification method to estimate confidence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections. We use a Viola and Jones AdaBoost cascade classifier for detection, a particle filter for tracking, and color histograms for appearance modeling. An experimental evaluation shows that our approach is capable of tracking humans in high density crowds despite occlusions.