Unusual events detection based on multi-dictionary sparse representation using kinect

Unusual events detection plays a crucial role in surveillance applications, which is becoming more and more urgent need for public security. However, illumination and scale changing, lacking of sufficient training data and subjective of abnormality definition are some of the severe difficulties, which are hard to deal with by widely used traditional cameras. In order to solve these problems, first, a novel feature is proposed in this paper, which is named random local feature (RLF) to describe the spatial-temporal information of depth image detected by the Kinect sensor. Then, we expand the sparse representation framework to a multi-dictionary sparse representation framework, based on the intuition that that anomaly of a same event may vary a lot in different regions in a scene. We split the depth video into several regions and use detected RLF features in each region to train dictionary by K-SVD algorithm, and use the OMP algorithm to sparse-represent each feature. Finally, an objective function is introduced to evaluate the anomaly of features in each region according to reconstruction errors. Unusual events are defined as those incidences that occur very rarely in the entire video sequence in our system, which is tested on real data and demonstrates promising results in unusual events detection.