Abstract: To solve the tracking drift problem caused by the low discriminability of sample information in typical correlation filtering framework, a correlation filter based object tracking algorithm with spatial structure information was proposed. Firstly, the spatial context structure constraint was introduced to optimize the model construction, meanwhile, the regularized least square and matrix decomposition idea were exploited to achieve the closed solution. Then, the complementary features were used for the target apparent description, and the scale factor pool was utilized to deal with target scale changing. Finally, according to the occlusion influence of target judged by motion continuity, the corresponding model updating strategy was designed. Experimental results demonstrate that compared with the traditional algorithm, the precision of the proposed algorithm is increased by 17.63%, and the success rate is improved by 24.93% in various typical test scenarios, achieving more robust tracking effect.

Key words: object tracking, correlation filter, spatial structure information, model optimization, updating strategy

摘要： 为解决在典型相关滤波框架模型中样本信息判别性低引起的跟踪漂移问题，提出一种利用空间结构信息的相关滤波目标跟踪算法。首先，引入空间上下文结构约束进行模型构建的优化，同时利用正则化最小二乘与矩阵分解思想实现闭式求解；然后，采用互补特征用于目标表观描述，并利用尺度因子池处理目标尺度变化情况；最后，借助目标运动连续性进行目标受遮挡影响情况的判定，设计相应的模型更新策略。实验结果表明，在多种典型测试场景中所提算法的准确率较传统算法提高了17.63%，成功率提高了24.93%，可以取得较为鲁棒的跟踪效果。

关键词: 目标跟踪, 相关滤波, 空间结构信息, 模型优化, 更新策略