Convolutional Sparse Coding-based Anomalous Event Detection in Surveillance Videos
Masanao Matsumoto, Naoki Saito, Takahiro Ogawa, Miki Haseyama · 2019
This paper presents a Convolutional Sparse Coding (CSC)-based anomalous event detection method in surveillance videos. The proposed method derives new features from reconstruction errors and sparse coefficient maps obtained by CSC, and the anomalous events are detected by a multi-layer network whose inputs are the above new features. Since such events, i.e., anomalous objects, have different characteristics in the sparse coefficient maps and their corresponding reconstruction errors, successful detection can be expected. Experimental results show high detection performance of own method.