一种鲁棒的目标跟踪方法

针对传统特征光流场跟踪方法中由于误差积累和错误匹配而导致的特征点丢失问题,基于一种新的Harris-SIFT特征点表示方法,提出基于预测帧与关键帧的算法框架,实现了光流场运动估计与局部特征识别相结合的目标跟踪方法.预测帧利用塔式分解和递归算法计算特征点的光流场运动矢量,使用运动矢量直方图获取目标的运动矢量,并剔除误匹配点;当特征点数量小于5个时,关键帧使用Harris-SIFT特征点进行局部特征匹配,利用仿射模型对目标精确定位及姿态修正.实验结果表明,本方法对视频序列中的纹理特征目标跟踪的鲁棒性较好,在背景复杂、目标遮挡或暂时丢失情况下,仍可以继续完成目标的可靠跟踪.

A Robust Object Detecting and Tracking Method

According to the problem of error accumulation and matched feature points loss in the optical flow feature tracking method, a predictive frame and key frame algorithm framework is proposed based on the new Harris-SIFT feature representation method. The proposed target tracking algorithm was realized by combining optical flow motion estimation and local feature recognition. Predictive frame uses pyramid decomposition and recursive algorithm to compute the motion vectors from optical flow field characteristics. The proposed algorithm gets motion vector of the target and eliminates false matching point from motion vector histogram; when the number of matched point is less than 5, the key frames uses the Harris-SIFT feature point for local feature matching, and affine model was used for accurate target positioning and attitude correction. The experiment results show that the proposed algorithm still can continue to achieve reliable tracking in complex background, target occlusion or temporarily lost case.