Title: A new crowd anomaly detection model: optimisation aided detection and localisation

Authors: Jyoti Ambadas Kendule; Kailash Jagannath Karande

Addresses: Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Pandharpur 413304, Maharashtra, India ' Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Pandharpur 413304, Maharashtra, India

Abstract: Detecting anomalies in crowded scenes is a challenging and important part of the intellectual video supervision system. In this work, a novel crowd anomaly detection model is developed that includes three main phases. Firstly, Entropy-based FCM (EFCM) is carried out to segment the frames from video. Further, Histogram of Gradients (HoG), Local Gradient Pattern (LGP) and improved motion estimation features via block matching technique features are derived. These features are then classified via hybrid classifiers that include DBN and LSTM models. The weights of DBN and LSTM classifiers are tuned in an optimal manner via Ant Lion Aided Grasshopper Optimisation (ALA-GO) model. The suggested HC + ALA-GO model achieved the minimal cost of 0.65 and provides faster convergence rate compared to HC + GOA, HC + SSO, HC + SMO, HC + ALO, HC + SSA and HC + SI-DOX models.

Keywords: crowded anomaly; EFCM; motion estimation; hybrid classifier; ALA-GO optimisation.

DOI: 10.1504/IJWMC.2025.148591

International Journal of Wireless and Mobile Computing, 2025 Vol.29 No.3, pp.256 - 269

Received: 16 Mar 2024
Accepted: 05 Aug 2024
Published online: 14 Sep 2025 *

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