Human Re-Identification In Crowd Videos Using Personal, Social And Environmental Constraints

Keywords

Dense crowds; Human tracking; Multiple cameras; Re-identification; Social constraints; Video surveillance

Abstract

This paper addresses the problem of human re-identification in videos of dense crowds. Re-identification in crowded scenes is a challenging problem due to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras. To solve this problem, we model multiple Personal, Social and Environmental (PSE) constraints on human motion across cameras in crowded scenes. The personal constraints include appearance and preferred speed of each individual, while the social influences are modeled by grouping and collision avoidance. Finally, the environmental constraints model the transition probabilities between gates (entrances/exits). We incorporate these constraints into an energy minimization for solving human re-identification. Assigning 1-1 correspondence while modeling PSE constraints is NP-hard. We optimize using a greedy local neighborhood search algorithm to restrict the search space of hypotheses. We evaluated the proposed approach on several thousand frames of PRID and Grand Central datasets, and obtained significantly better results compared to existing methods.

Publication Date

1-1-2016

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

9906 LNCS

Number of Pages

119-136

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-46475-6_8

Socpus ID

84990848493 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84990848493

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