Unsupervised detection of nonlinearity in motion using weighted average of non-extensive entropies

Motion estimation and motion analysis have an important role to play for detecting abnormal motion in surveillance videos. In this paper, we propose to use the non-extensive entropy to detect any unnaturalness in the motion over correlated video frames since it has already been proved to represent the correlated textures successfully. To achieve this end, we utilize the temporal correlation property of motion vectors over three consecutive frames to detect any motion disturbance using a weighted average of the non-extensive entropies. It is proved by the experimental results on the state-of-the-art database that the non-extensive entropy is most apt for detecting any disturbance in the continuance of motion vectors in between frames. The advantage of our approach is that no training period or normalcy reference is required since a relative disturbance in the magnitudes of motion vectors over a three-frame window gives an alarm.