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Article type: Research Article
Authors: Mukesh Krishnan, M.a; * | Thanga Ramya, S.b | Ramar, K.c
Affiliations: [a] Department of Computer Science and Engineering, Francis Xavier Engineering College, Vannarpettai, Tirunelveli, Tamilnadu, India | [b] Department of Computer Science and Design, RMK Engineering College, Kavarapettai, Tiruvallur, Tamilnadu, India | [c] Department of Computer Science and Engineering, RMK College of Engineering and Technology, Pudovoyal, Tiruvallur, Tamilnadu, India
Correspondence: [*] Corresponding author. M. Mukesh Krishnan, Assistant Professor, Department of Computer Science and Engineering, Francis Xavier Engineering College, Vannarpettai, Tirunelveli-627003, Tamilnadu, India. E-mail: mukeshkrishnan91.m@gmail.com.
Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods.
Keywords: Unusual event detection, crowd detection, crowd clustering, and unusual crowd activities model
DOI: 10.3233/JIFS-233833
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3789-3798, 2024
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