A Review of Abnormal Behaviour Detection in Crowd for Video Surveillance: Advances and Trends, Datasets, Opportunities and Prospects

The detection of abnormal behaviours with fast and automatic recognising is significant in crowded areas to provide higher security to the public. The adoption of deep learning and machine learning-based abnormal behaviour detection models enhances the influential detection and real-time security monitoring in crowds. The researchers have remotely evaluated the heart rate based on physiological information to detect abnormal activities in various years. Over the past few years, several progress have been made, and there are still some issues concerning processing time, accuracy, and computational complexity. The developed approaches detects the activities of anomalies like traffic rule violations, riots, fighting, and stampede, in addition, several anomalous entities such as abandoned luggage and weapons at the sensitive place automatically in time. However, the identification of video anomalies methods poses several challenges because of various environmental conditions, the ambiguous nature of the anomaly, lack of proper datasets, and the complex nature of human characteristics. In recent days, there have been only a few devoted surveys associated with deep learning related video anomaly identification as the research domain is in its initial stages. In this review work, the abnormal behaviour analysis models using deep learning are reviewed in depth in for security applications. Based on the traditionally used abnormal behaviour analysis models in crowded scenes, we widely categorised the methods into classification using object tracking, classification using handcrafted extracted features, classification using non-contact heart rate variability and blood pressure, analysing motion patterns from the visual frames, and classification using face images. We also discuss the comparative analysis of the previous methods with respect to datasets, computational infrastructure, and performance measures for both qualitative and quantitative analysis. In addition, the open and trending research challenges are analysed for future research.