Unsupervised video anomaly detection using 3D CNNs and novelty detection techniques

Unsupervised/semi-Supervised video classification When there are hundreds or thousands of cameras producing video streams all day long it is very useful to have an algorithm that analyzes such streams instead of a human. Today such technology exists and is called convolutional neural networks for video classification. The downside of such neural networks is that we have a fixed number of cases on which the net is trained which is ok for benchmarking our algorithm on a specific dataset but not for real life applications such as security cameras where we don’t know specifically for which scene the algorithm should give an alert signal. So we need to produce an abstract representation of the video scene (embedding) and to classify it in an unsupervised way.