This research employs CiteSpace software to analyze research outcomes on Abnormal Behavior Detection Based on Video Surveillance. It focuses on the China National Knowledge Infrastructure Database (CNKI) and the "Web of Science" Database, aiming to identify research trends, provide some references for advancing this research direction. The following conclusions are drawn: (1) The literature on abnormal behavior detection models and algorithms has shown a consistent increase over time. (2) The current research trend is deep learning, video surveillance and abnormal detection. Research tends to diversify. (3) In China and abroad, there is more attention in the field of deep learning, video surveillance and anomaly detection.
This research reviewed the rapidly developing field of abnormal behavior detection in video surveillance. The emergence of deep learning techniques, especially in video feature extraction, makes up for the shortcomings of traditional methods. In terms of abnormal behavior detection, unsupervised, supervised, and weakly supervised methods have different advantages and disadvantages. At present, the weakly supervised method is popular in this field, and the highest AUC under the UCF dataset reaches 86.98%. Benchmark datasets play a crucial role in evaluating the performance of algorithms. Future research will focus on addressing challenges related to complex scene dynamics, occlusions, and real-time processing. Integrating multiple sensing modalities, transfer learning, deep learning techniques for feature extraction, and leveraging spatiotemporal graph-based methods are important directions for improving surveillance systems in anomaly detection.
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