Multi-scale crowd feature detection using vision sensing and statistical mechanics principles

Authors: Arbab-Zavar, B. and Sabeur, Z.

http://eprints.bournemouth.ac.uk/33857/

Journal: Machine Vision and Applications: an international journal

Publisher: Springer Nature

ISSN: 0932-8092

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.

This data was imported from Scopus:

Authors: Arbab-Zavar, B. and Sabeur, Z.A.

http://eprints.bournemouth.ac.uk/33857/

Journal: Machine Vision and Applications

Volume: 31

Issue: 4

eISSN: 1432-1769

ISSN: 0932-8092

DOI: 10.1007/s00138-020-01075-4

© 2020, The Author(s). 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 categorize 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 are further discussed in detail in this paper.

This data was imported from Web of Science (Lite):

Authors: Arbab-Zavar, B. and Sabeur, Z.A.

http://eprints.bournemouth.ac.uk/33857/

Journal: MACHINE VISION AND APPLICATIONS

Volume: 31

Issue: 4

eISSN: 1432-1769

ISSN: 0932-8092

DOI: 10.1007/s00138-020-01075-4

The data on this page was last updated at 05:19 on January 20, 2021.