Periodicity Detection of Local Motion

Periodicity is useful for compact representation of periodic motion and a reasonable selection of a proper temporal scale for periodic motion analysis. In this paper, we concern the periodicity detection of local motion within an interesting region and present an approach to automatically detect the motion periodicity inherent to local motion under complex condition. The task is challenging as local motion is usually buried in clutters with global motion and noises. Most existing methods have assumed a static camera and a labeled moving object region. We instead apply robust local motion estimation and an object localization method to extract the object motion. The object motion is characterized by the confidence based motion probability map and the motion vectors obtained by global motion compensation. The autocorrelation series of motion energy is then carried out to locate local maximum points. With the set of indices of local maximum points, we can estimate the basic periodicity through a least-square fitting. This method has been applied to swimming videos and got encouraging results