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Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Title
Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier
Author
김기범
Issue Date
2021-05
Publisher
MDPI
Citation
ENTROPY, v. 23, No. 5, Article no. 628, 26pp
Abstract
To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today's complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter
URI
https://www.proquest.com/docview/2532344994?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/166764
ISSN
1099-4300
DOI
10.3390/e23050628
Appears in Collections:
ETC[S] > 연구정보
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