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Multi-scale Crowd Feature Detection using Vision Sensing and Statistical Mechanics Principles.

Arbab-Zavar, B. and Sabeur, Z., 2020. Multi-scale Crowd Feature Detection using Vision Sensing and Statistical Mechanics Principles. Machine Vision and Applications, 31 (4), 26.

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DOI: 10.1007/s00138-020-01075-4

Abstract

Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorise various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate interpretation is a challenging problem. In this paper, analogies between human crowds and molecular thermodynamics systems are drawn for the measurement of crowd behaviour. Specifically, a novel descriptor is defined and measured for crowd behaviour at multiple scales. This descriptor uses the concept of Entropy for evaluating the state of crowd disorder. By results, the descriptor Entropy does indeed appear to capture the desired outcome for crowd entropy while utilizing easily detectable image features. Our new approach for machine understanding of crowd behaviour is promising, while it offers new complementary capabilities to the existing crowd descriptors, for example, as will be demonstrated, in the case of spectator crowds. The scope and performance of this descriptor is further discussed in details in this paper.

Item Type:Article
ISSN:0932-8092
Uncontrolled Keywords:Crowd dynamics; Crowd behaviour detection; Multi-scale crowd features; Group detection and tracking; Video analysis Statistical mechanics
Group:Faculty of Science & Technology
ID Code:33857
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Apr 2020 15:20
Last Modified:11 May 2020 13:55

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