An Unsupervised Anomalous Event Detection Framework with Class Aware Source Separation

This paper presents a novel problem of detection and localization of anomalous events due to a certain class of objects in video data with applications to smart surveillance. A baseline system is proposed that uses a convolutional neural network (CNN) to generate pixel level masks corresponding to objects of a class of interest. A Restricted Boltzmann Machine (RBM) is then trained on the mask to learn patterns of normal behavior. The free energy of the RBM is used to detect the presence of an anomaly while the reconstruction error is used to localize the anomaly. Our approach is scalable to a low power and energy constrained setting with 1930.48 ms of latency and 4826 mJ energy consumed per frame on a mGPU.