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Deep anomaly detection through visual attention in surveillance videos
2020
Journal of Big Data
This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning. In our proposed method, a robust background subtraction (BG) for extracting motion, indicating the location of attention regions is employed. The resulting regions are finally fed into a three-dimensional Convolutional Neural Network (3D CNN). Specifically, by taking advantage of C3D (Convolution 3-dimensional), to
doi:10.1186/s40537-020-00365-y
fatcat:cn2dsuzxlrg7db3qyqlxqihvia