Analyzing crowd behavior in naturalistic conditions: Identifying sources and sinks and characterizing main flows

Pedestrians, in videos taken from fixed cameras, tend to appear and disappear at precise locations such as doors, gateways or edges of the scene: we refer to locations where pedestrians appear as sources (potential origins) and the locations where they disappear as sinks (potential destinations). Th...

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Vydáno v:	Neurocomputing Ročník 177; s. 543 - 563
Hlavní autoři:	Khan, Sultan D., Bandini, Stefania, Basalamah, Saleh, Vizzari, Giuseppe
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 12.02.2016 Elsevier BV
Témata:	Crowd analysis Crowd analysis; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive Neuroscience Crowd analysis
ISSN:	0925-2312, 1872-8286
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Pedestrians, in videos taken from fixed cameras, tend to appear and disappear at precise locations such as doors, gateways or edges of the scene: we refer to locations where pedestrians appear as sources (potential origins) and the locations where they disappear as sinks (potential destinations). The detection of these points and the characterization of the flows connecting them represent a typical preliminary step in most pedestrian studies and it can be supported by computer vision approaches. In this paper we propose an algorithm in which a scene is overlaid by a grid of particles initializing a dynamical system defined by optical flow, a high level global motion information. Time integration of the dynamical system produces short particle trajectories (tracklets), representing dense but short motion patterns in segments of the scene; tracklets are then extended into longer tracks that are grouped using an unsupervised clustering algorithm, where the similarity is measured by the Longest Common Subsequence. The analysis of these clusters supports the identification of sources and sinks related to a single video segment. Local segment information is finally combined to achieve a global set of traces identifying sources and sinks, and characterizing the flow of pedestrians connecting them. The paper presents the defined technique and it discusses its application in a real-world scenario.
ISSN:	0925-2312 1872-8286
DOI:	10.1016/j.neucom.2015.11.049