为了增强目标跟踪算法在被跟踪目标发生运动位移、遮挡、形变、相似物体干扰等情况下的鲁棒性, 提出利用超像素构建目标外观模型, 将外观模型与候选区域进行匹配, 获取候选区域当中目标超像素, 并用Meanshift算法确定目标中心点的跟踪算法。仿真实验选取Benchmark库当中在运动位移、遮挡、形变、相似物体干扰方面具有代表性的视频Girl和FaceOcc1。该算法在视频Girl中的跟踪成功率和跟踪精度为0.601、0.856, 比对比实验的经典算法当中跟踪效果最好的KCF算法的成功率和精度分别高0.059和0.084; 在视频FaceOcc1中跟踪成功率和精度仅次于KCF。表明该跟踪算法在受到相似物体干扰和目标遮挡时具有良好的鲁棒性。

In order to enhance robustness of target tracking algorithm under conditions of motion displacement, occlusion, deformation and similar object disturbance, it is proposed to construct target appearance model by using super pixel, and match appearance model with candidate region to obtain candidate region target super pixel, and use Meanshift algorithm to determine target center point tracking algorithm. Simulation experiments select representative of video Girl and FaceOcc1 from Benchmark library, which represent video scene in terms of movement displacement, occlusion, deformation, interference of similar objects. Tracking success rate and tracking accuracy of algorithm are 0.601 and 0.856 in video Girl, and success rate and accuracy of KCF algorithm with best tracking performance are higher than normal algorithm of 0.059 and 0.084 respectively. In video FaceOcc1, tracking success rate and accuracy of proposed algorithm only ranked second to KCF, suggesting a fine robustness even when target is blocked or interfered by analogues.