Browsing by Author "Santosh Kumar Tripathy"

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A review of deep learning approaches for video-based crowd anomaly detection
(Institution of Engineering and Technology, 2023) Santosh Kumar Tripathy; Rajeev Srivastava
In recent years, the video surveillance system has gained huge demand in public and private places to provide security and safety. Video-based crowd anomaly detection (VCAD) is one of the crucial applications of a surveillance system whose timely detection and localization can prevent massive loss of public or private properties and the lives of many people. Crowd anomalies or abnormal activities can be defined as irregular activities that deviate from normal crowd behavior patterns. Some abnormal activities in crowd scenes are panic, fights, stampedes, congestion, riots, and abandoned luggage, whose real-time detection is paramount. The crowd anomaly detection (CAD) becomes a more challenging task due to the dynamic nature of the crowd, the effect of the cluttered background, daylight changes, shape variation due to perspective distortion, and lack of large-scale ground-truth crowd datasets. Both conventional machine learning and deep learning approaches have been explored to provide different solutions for CAD. The current research trend shows the vast development of deep-learning approaches for CAD. However, state-of-the-art reviews still need to address the comprehensive analysis of deep models, performance evaluation methodologies, open issues, and challenges for VCAD. Therefore, the main objective of this review is to provide an insightful analysis of several deep models for VCAD, their comparative analysis on different datasets based on various performance metrics, and to discuss future research scope for VCAD. © The Institution of Engineering and Technology 2023. All rights reserved.