Structured Kalman Filter for Tracking Partially Occluded Moving Objects

Moving object tracking is one of the most important techniques in motion analysis and understanding, and it has many difficult problems to solve. Especially estimating and tracking moving objects, when the background and moving objects vary dynamically, are very difficult. The Kalman filter has been used to estimate motion information and use the information in predicting the appearance of targets in succeeding frames. It is possible under such a complex environment that targets may disappear totally or partially due to occlusion by other objects. In this paper, we propose another version of the Kalman filter, to be called Structured Kalman filter, which can successfully work its role of estimating motion information under such a deteriorating condition as occlusion. Experimental results show that the suggested approach is very effective in estimating and tracking non-rigid moving objects reliably.