Frame-skipping tracking for single object with global motion detection

Frame-skipping videos usually appear in wireless video sensor networks which have wirelessly interconnected devices that are able to ubiquitously retrieve video content from the environment. Frame-skipping videos bring to difficulties in getting the transition model (how objects move between frames). We propose a particle filter with global motion detection requiring no offline or online learning. Experimental results show the proposed approach improves the tracking accuracy in comparison with the existing conventional methods, under the condition of frame skipping data and motion of both targets and video sensors.