基于SAD与UKF-Mean Shift的主动目标跟踪

Automatic Object Tracking Method Based on SAD and UKF-Mean Shift

针对复杂场景下动态目标难以准确分割以及目标难以准确定位的问题,提出将绝对差值和(SAD)方法、无迹卡尔曼滤波(UKF)和Mean shift算法相结合的混合自主跟踪动态目标的方法.首先,采用SAD方法获相邻两帧的视差信息,利用视差实现动态目标的检测,并依此建立目标的核直方图描述模型和状态空间模型,然后UKF算法对状态空间进行滤波估计,最后采用Mean shift算法精确定位目标.实验结果表明该方法不仅能有效检测场景的动态目标,同时还能获得目标的运动信息.文中所提出的基于UKF-Mean shift的跟踪策略与相关算法相比,体现出较好的跟踪效果与时间性能.

Aiming at the problems in accurate motion detection and tracking location under complex scene, an automatic object tracking method combined sum of absolute difference （SAD） and Mean shift with unscented Kalman filter （UKF） is proposed. Firstly block matching method based on SAD is used to estimate the displacement between current frame and successive frame. Then the disparity cues are utilized to detect the moving object automatically and build the object model and state-space model for following tracking task. Finally Mean shift with UKF is employed to filter and estimate the state of the object and locate the object in subsequence image frame. The experimental results show that the proposed moving object detection method effectively detects moving objects in scene and acquires the motion information of objects. Compared with the related methods, the proposed tracking strategy based on UKF-Mean shift has better tracking results and time property.