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Spatiotemporal representation learning for video anomaly detection
2020
IEEE Access
Video-based anomalous human behavior detection is widely studied in many fields such as security, medical care, education, and energy. However, there are still some open problems in anomalous behavior detection, such as the large and complicated model is difficult to train, the accuracy of anomalous behavior detection is not high enough and the speed is not fast enough. A spatiotemporal representation learning model is proposed in this paper. Firstly, the spatial-temporal features of the video
doi:10.1109/access.2020.2970497
fatcat:tlaxd6pxxngczkvzm3sz3aqcvu