Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory

This paper presents a novel deep learning-based approach for anomaly detec- tion in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested ap- proach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and con- volutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is em- ployed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XD- Violence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with Con- vLSTM can increase precision and reduce false positives, achieving a high ac- curacy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cutting- edge techniques mentioned in the comparison.