Region Based Anomaly Detection with Real-Time Training and Analysis

We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region proposal mechanism used in [9] with one that makes the overall method more efficient. where they generate thousands of regions per image, we generate far fewer but better targeted regions. We also propose a 'convolutional' variant which does away with region extraction altogether, and propose improvements to the density estimation phase used in both variants.