中文摘要：

为解决目标在复杂环境下表观信息判别性低引起的跟踪漂移问题，提出一种利用物体性检测的目标跟踪算法．该算法首先依据核相关滤波器初步求得目标预测状态；然后，基于物体建议边界框检测原理，生成考虑尺度大小与纵横比的初始建议边界框集合，并设计精选择准则得到优化的建议边界框集合；通过引入运动连续性，求得基于建议边界框的目标预测位置和尺度，进而综合求得最佳目标状态估计；最后，对当前帧目标进行遮挡影响判定，给出相应的模板更新策略．实验结果表明，新算法在多种典型测试场景中都能取得较为鲁棒的跟踪效果．

英文摘要：

To solve the tracking drift problem caused by the low discrimination of object appearance information in complex environment, the paper proposes an object tracking algorithm using objectness detection. First, the algorithm obtains the preliminary object prediction state with kernelized correlation filters. Then, according to the objectness detection principle of the proposal bounding box, it generates the original proposal bounding box set with the consideration of the scale and aspect ratio, and further gets optimized sets with the refined selection criterion. By introducing motion continuity, the prediction location and scale based on the proposal bounding box are calculated, and then the final optimum object state estimation is acquired comprehensively. Finally, taking into account the occlusion influence judge of target appearance at the current frame, the corresponding template updating scheme is given. Experimental results demonstrate that the novel algorithm achieves robust tracking performance in various typical testing scenarios.