改进Mean Shift目标跟踪算法实现

目标跟踪技术目前具有较好的应用前景，但在嵌入式处理平台中面临着实时性要求高、跟踪场景复杂等情况，加上受成本和嵌入式处理平台算力的限制，其处理效果往往很难满足现实需求，因此目标跟踪等图像处理技术的落地实现是当前研究的热点内容。针对此问题，本文在FPGA平台实现了改进Mean Shift目标跟踪算法，该算法首先通过目标的概率密度分布梯度爬升来寻找目标，然后采用Kalman滤波的预测机制来预估下一帧搜寻计算的位置，从而减少Mean Shift的迭代次数。该算法实现充分利用FPGA能够并行和流水线处理的特点，实现了在1 920×1 080@60 Hz高清视频图像场景下的实时目标跟踪，其中Kalman滤波算法使其在较复杂场景下也能具备一定的抗遮挡干扰的能力。

Improved Mean Shift target tracking algorithmz

Target tracking technology has good application prospect at present, but in an embedded processing platform is facing complicated high real-time requirements, tracking, etc., and restricted by cost and embedded processing platform to calculate force, it is often difficult to meet the demand of reality of its processing effect, so the image processing technology such as target tracking ground implementation is a hotspot of current research content. To solve this problem, this paper implemented an improved Mean Shift target tracking algorithm on FPGA platform. The algorithm first searched for the target by the gradient climbing of the probability density distribution of the target, and then used the prediction mechanism of Kalman filter to predict the location of the next frame search calculation, so as to reduce the number of iterations of Mean Shift. The algorithm makes full use of the parallel and pipelined-processing characteristics of FPGA to realize the real-time target tracking in 1 920×1 080@60 Hz hd video image scene, and the Kalman filtering algorithm enables it to have a certain ability to resist occlusion interference in more complex scenes.