Abstract: A distortion-aware correlation filter object tracking algorithm is proposed to address the problem that the existing correlation filters have insufficient ability to deal with target distortion and the filter model updating error accumulation easily leads to tracking failure. Firstly, particle sampling is used to construct a spatial reference weight for enhancing the target information and adapt to changes in the target appearance between adjacent frames so that the filter is focused on the reliable part of the learning target and the interference of background information is suppressed. Meanwhile, to optimize the algorithm and reduce computational complexity, the alternating direction multiplier method is used to solve the objective optimal function value with fewer iterations. Finally, to further enhance the discrimination ability of the filter, a target distortion-aware strategy is designed, which combines the average peak correlation energy and the response map peak temporal constrain to measure the distortion of the target affected by interference factors and to determine whether the current tracking result is reliable. When the reliability of target tracking and positioning is low, the particle filter is used to selectively re-detect the target. Depending on the extent of distortion of the tracking target at any given time, the filter model is adaptively updated. Compared  with various representative correlation filters on the OTB50, OTB100, and DTB70 datasets, the experimental results show that the tracking success rate and precision of the distortion-aware correlation filter object tracking algorithm are the best, and it has strong robustness in the face of targets distorted by multiple interference factors in the actual scene.

Key words: object tracking, particle filter, correlation filter, adaptive spatial regularization, distortion-aware

摘要： 针对现有相关滤波跟踪算法在目标畸变情况下应对能力不足和滤波器模型更新存在误差累积易导致跟踪失败的问题，提出畸变感知相关滤波目标跟踪算法。首先，利用粒子采样构建强化目标信息的空间参考权值，适应相邻帧间目标外观变化，使滤波器专注于学习目标可信赖部分，抑制背景干扰信息；其次，采用交替方向乘子法以较少的迭代次数求解目标最优函数值，优化算法，降低计算复杂度；最后，为进一步增强滤波器的判别能力，设计目标畸变感知策略，通过分析平均峰值相关能量和响应图峰值时序约束来衡量目标受干扰因素影响后的畸变程度，判别当前跟踪结果是否可靠。当目标跟踪定位可靠性较低时，采用粒子滤波对目标进行选择性的重检测。并依据当前跟踪目标畸变程度，自适应地更新滤波器模型。在OTB50、OTB100和DTB70数据集上与多种代表性目标跟踪算法进行对比实验，实验结果表明，该算法的跟踪成功率和精确率较优，在面对实际场景中因多个干扰因素而产生畸变的目标时具有较强鲁棒性。

关键词: 目标跟踪, 粒子滤波, 相关滤波, 自适应空间正则项, 畸变感知