Detecting Humans In Dense Crowds Using Locally-Consistent Scale Prior And Global Occlusion Reasoning

Keywords

combinations-of-parts detection; crowd analysis; deformable parts model; dense crowds; global occlusion reasoning; human detection; locally-consistent scale prior; Markov Random Field; scale context; spatial priors

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 and occlusion reasoning in proposed approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. We performed experiments on a new and extremely challenging dataset of dense crowd images showing marked improvement over the underlying human detector.

Publication Date

10-1-2015

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

37

Issue

10

Number of Pages

1986-1998

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2015.2396051

Socpus ID

84941194361 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84941194361

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