Target tracking with improved CAMShift based on Kalman predictor

CAMShift(Continuously Adaptive Mean Shift) target tracking algorithm is liable to fall into a local maxima when the target is occluded, and is prone to failure when the targets move fast, and can not be recovered from the failure. To solve this problem, the CAMShif algorithm is improved by using Kalman predictor. First, the position of the target in the next frame image is predicted by using the Kalman predictor and this position is used as the center to determine the searching area of CAMShift target tracking algorithm. Then the Bhattacharyya coefficient of target matching and the size of the target are utilized to determine whether the target is occluded and the degree of occlusion. If not occluded, the parameters of Kalman predictor are updated by the position of the target with CAMShift algorithm. If the occlusion is not serious, the current location and size of the target are determined by the predictive values of Kalman predictor, and this set of values are used to update the parameters of Kalman predictor. If the occlusion is very serious, the current location is determined by the predictive values of the Kalman predictor and the target size is a fixed value, then this set of values are used to update the parameters of Kalman predictor. The experimental results show that the improved algorithm is able to accurately track the occluded and/or fast moving targets.