Foreground Segmentation for Anomaly Detection in Surveillance Videos Using Deep Residual Networks

Efficient anomaly detection in surveillance videos across diverse environments represents a major challenge in Computer Vision. This paper proposes a background subtraction approach based on the recent deep learning framework of residual neural networks that is capable of detecting multiple objects of different sizes by pixel-wise foreground segmentation. The proposed algorithm takes as input a reference (anomalyfree) and a target frame, both temporally aligned, and outputs a segmentation map of same spatial resolution where the highlighted pixels denoting the detected anomalies, which should be all the elements not present in the reference frame. Furthermore, we analyze the benefits of different reconstruction methods to the restore original image resolution and demonstrate the improvement of residual architectures over the smaller and simpler models proposed by previous similar works. Experiments show competitive performance in the tested dataset, as well as real-time processing capability.