Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Haroon IdreesMubarak Shah

Abstract

Human detection in dense crowds is an important problem, as it is a prerequisite to many other visual tasks, such as tracking, counting, action recognition or anomaly detection in behaviors exhibited by individuals in a dense crowd. This problem is challenging due to the large number of individuals, small apparent size, severe occlusions and perspective distortion. However, crowded scenes also offer contextual constraints that can be used to tackle these challenges. In this paper, we explore context for human detection in dense crowds in the form of a locally-consistent scale prior which captures the similarity in scale in local neighborhoods and its smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detection hypotheses are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection a...Continue Reading

References

May 22, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·David GerónimoThorsten Graf
Jul 17, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro F FelzenszwalbDeva Ramanan
Aug 3, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Piotr DollárPietro Perona
Aug 17, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Louis Kratz, Ko Nishino

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Citations

Mar 19, 2016·IEEE Transactions on Cybernetics·Ven Jyn Kok, Chee Seng Chan

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