Taxonomy of Anomaly Detection Techniques in Crowd Scenes

Abstract

With the widespread use of closed-circuit television (CCTV) surveillance systems in public areas, crowd anomaly detection has become an increasingly critical aspect of the intelligent video surveillance system. It requires workforce and continuous attention to decide on the captured event, which is hard to perform by individuals. The available literature on human action detection includes various approaches to detect abnormal crowd behavior, which is articulated as an outlier detection problem. This paper presents a detailed review of the recent development of anomaly detection methods from the perspectives of computer vision on different available datasets. A new taxonomic organization of existing works in crowd analysis and anomaly detection has been introduced. A summarization of existing reviews and datasets related to anomaly detection has been listed. It covers an overview of different crowd concepts, including mass gathering events analysis and challenges, types of anomalies, and surveillance systems. Additionally, research trends and future work prospects have been analyzed.

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“…This paper concludes with a discussion of future research directions in crowd anomaly detection. A detailed review of the anomaly detection methods in crowd scenes from the computer vision perspective was presented in [1], which focused on studying the human crowd, specifically abnormal human behaviour. The paper summarised the state-of-the-art anomaly detection methods in crowd scenes and categorised them based on their approach, anomaly scope, and processing target.…”

“…The phenomenon of crowds has garnered considerable academic interest in recent years owing to the proliferation of events that attract large gatherings [1]. The safety concerns associated with such events, particularly religious and sporting events, have underlined the importance of detecting crowd anomalies, which entails examining the actions and interactions of individuals in large groups.…”

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