Periodic motion detection with ROI-based similarity measure and extrema-based reference-frame selection

This paper presents a new algorithm for detecting and analyzing the periodic motions in video sequences. Different from the previous methods which detect periodic motions from the entire frame, we propose a convex-hull-based process to automatically determine the regions of interest (ROI) of the motions and utilize an ROI-based similarity measure to detect the motion periods. Furthermore, we also propose an extrema-based method to select the optimal reference frame for further improving the periodic detection performance. Our proposed algorithm can not only effectively detect motion periods with both constant and variable period lengths, but also have obvious advantage when handling periodic motion with slight movements. Experimental results demonstrate the effectiveness of our proposed method.