基于Kalman预测器的改进的CAMShift目标跟踪

Target tracking with improved CAMShift based on Kalman predictor

CAMShift目标跟踪算法遇到目标被遮挡时容易陷入局部最大值,对快速运动目标容易跟踪失败,且无法从失败中复原。针对该问题,利用Kalman预测器改进CAMShift算法。首先利用Kalman预测器预测下帧图像中目标的位置,以此位置为中心确定CAMShift算法进行目标跟踪的搜索区域;然后利用目标匹配时的Bhattacharyya系数及目标大小来判断目标是否被遮挡以及被遮挡的程度。如果没有被遮挡,则用CAMShift算法得到的目标位置更新Kalman预测器中参数;如果遮挡不严重,则用Kalman预测器的预测值作为目标的位置和大小,且用该组值更新Kalman预测器中参数;如果遮挡非常严重,则用Kalman预测器的预测值作为目标当前位置,目标大小为固定值,用该组值更新Kalman预测器中参数。实验结果表明,改进算法能够准确地跟踪被遮挡目标和快速运动目标。

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.