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2016 | OriginalPaper | Buchkapitel

Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints

verfasst von : Shayan Modiri Assari, Haroon Idrees, Mubarak Shah

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Playing for Data: Ground Truth from Computer Games

Nächstes Kapitel Revisiting Additive Quantization

Data and ground truth available at: http://crcv.ucf.edu/projects/Crowd-Reidentification.

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Titel: Human Re-identification in Crowd Videos Using Personal, Social and Environmental Constraints
verfasst von: Shayan Modiri Assari
Haroon Idrees
Mubarak Shah
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46475-6_8

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