基于关键区域特征匹配的视觉跟踪算法

针对视觉跟踪中目标表观的复杂变化问题,提出了一种基于关键区域特征匹配的鲁棒跟踪算法.首先对目标模板进行初始化并通过滤波预测得到目标候选;然后采用自适应标记分水岭算法对目标模板和目标候选进行分割以提取关键区域,并利用像素的空间和频率分布特性对关键区域进行多重特征描述;最后通过关键区域的特征匹配得到目标模板与目标候选的匹配关系,由此确定最终跟踪结果并进行模板更新.对目标发生尺度、遮挡、旋转、光照、姿态、复杂背景以及运动模糊等变化的视频序列进行了仿真测试.实验结果表明,所提算法能够有效处理目标表观的复杂变化问题,尤其对目标的部分遮挡、光照变化以及复杂背景等具有较强的鲁棒性.

Visual Tracking Algorithm Based on Feature Matching of Key Regions

In order to cope with the complex variation of target appearance during visual tracking,a robust tracking algorithm based on feature matching of key regions is proposed.Firstly,it initializes the target model and obtains target candidate through filter prediction.Then,it extracts the key regions of target model and target candidate using adaptive marker-based watershed algorithm and describes them with multiple features.Finally,it matches the key regions to get the mapping from target model to target candidate and calculates the final tracking results to output and update the target model.The proposed algorithm is tested on the video database containing the appearance variation of scale,occlusion,rotation,illumination,pose,background clutters,and motion blur.The experimental results demonstrate that the proposed algorithm can well cope with the complex appearance variation,especially shows the robustness to the partial occlusion,illumination and background clutters.