Real-time statistical anomaly detection in video

EhEye is a surveillance recognition software company specializing in public safety and video surveillance by*using analytics and artificial intelligence. Surveillance video cameras are now widely used, however,*surveillance systems are often ineffective due to the need for experienced individuals' continuous attention to a*large number of videos. Automatic and fast detection of anomalous behaviour of individuals, such as fighting,*in a sequence of video frames offers new exciting possibilities for improving this effectiveness, especially for*large, critical infrastructure locations. This will provide a system which can optimize the capabilities of*existing security camera infrastructure and increase the security team's ability to respond to situations in real*time.*There are, however, a number of technical challenges in implementing such an automatic system. This project*will address the two key challenges - namely fast preprocessing of the data and accurate detection of anomalies*in video stream. First, the high dimensional data in each video frame needs to be reduced to the most relevant*low dimensional information needed for effective detection; the computation of this low dimensional*information needs to be performed in real time as video proceeds. Second, an accurate model needs to be*trained based on the extracted low dimensional information to estimate the probability of the fighting scenes*and conclude on it. The model or method for such prediction can be trained off-line but need to run in real time*when applied online.