Research On The Tracking Method Based On Meanshift For Human-Object On Video

The moving human tracking technology in video has become an important topic andresearch focus in computer vision.This paper has researhed some represented trackingmethods such as optical flow,Kalman Filter,Particle filter and Meanshift Algorithm.The ideaof Meanshift is to find the position of target through multiple iterations drift. It could be easilyrealized to get accurte tracking position for low-speed targets,but not suitable for tracking fastmoving target.There is also accumulated error to lost target in Tracking for Meanshift.In this paper,we proposed a new Meanshift tracking method combined with Kalman filterand frame difference method. Kalman filter could predict target’s centroid and scaling. Framedifference method can detect and get the information of movement areas. So the new methodguides Meanshift algorithm kernel tracking effectively in its drift process. Experiments showthe new method reduces the tracking time and is robust for long period tracking offast-moving targets.In the last chapter,we expand to research and design an action recognitionmethod based on three feature descriptors.Finally, we introduce the innovation of this study again:1) Design a new Meanshifttracking method with combination of Kalman filter and frame difference method.2) The newmethod is robust for fast-moving target in tracking with less time.3) Propose a frame-skippingtarget-tracking method.4) Propose and designe an action recognition method based on threefeatures of the limb descriptor.