Recent Advancements in Deep Learning for Crowd Anomaly Detection: A Comprehensive Survey
Keywords:
Crowd behaviour analysis, Autonomous system, Crowd anomaly, Surveillance videos, Deep learningAbstract
Recent necessary events on our globe have drawn tons of attention to the necessity of autonomous crowd activity analysis. In computer vision and cognitive science, crowd behaviour analysis has piqued people's curiosity. Consider a watchman observing an oversized set of CCTV camera footage from police investigation cameras. The guard cannot possibly concentrate on all or any of the cameras for long periods of time. The COVID-19 breakout sessions and public events, for example, necessitate an automated system to manage, count, secure, and track a crowd that occupies the same space. Due to significant occlusion, complicated actions, and posture changes, assessing crowd situations is difficult. An algorithm that monitored each video and flagged probably strange activity mechanically would alter the guard to perform his duties with inflated accuracy and at large scale. This is often the motivation behind video anomaly detection. The basic idea is to learn a model of normal activity given training video of normal activity and then to use this model to detect anomalies, which are activities that are different from any seen in the training video. The training video cannot be expected to also contain anomalies simply because one cannot possibly know or capture all possible future anomalous events. Deep learning is a subset of machine learning that employs artificial neural networks to learn tasks from data. It has been used for various applications such as image classification, object detection, and natural language processing. In recent years, deep learning has also been applied to crowd anomaly detection Crowd anomaly detection is a difficult but important problem. Recently, deep learning has shown great promise in solving this problem. Deep learning anomaly detection technologies overruled the traditional machine learning systems. In this survey, we will briefly exhaustively overview of deep learning-based video anomaly detection systems that have been released since 2019.
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