No abstract

This article proposes a method to detect abnormal motion based on the subdivision of regions of interest in the scene. The method reduces the large amount of data generated in a tracking-based approach as well as the corresponding computational cost in training phase. The regions are spatially identified and contain data of transition vectors, resulting from the centroid tracking of multiple moving objects. On these data, we applied a one-class supervised training with one set of normal tracks on Gaussian mixtures to find relevant clusters, which discriminate the trajectory of objects. The lowest probability of transition vectors is used as the threshold to detect abnormal motions. The ROC (Receiver Operating Characteristic) curves are used to this task and also to determinate the efficiency of the model for each size increment of the region grid. The results show that there is a range of grid size values, which ensure a best margin of correct abnormal motions detection for each type of scenario, even with a significant reduction of data samples.