针对相关滤波类跟踪算法难以解决的过度形变和目标被遮挡问题, 提出了一种融合改进均方峰值旁瓣和客观相似性度量的高置信度跟踪算法-HCF。基于核相关滤波跟踪算法, 结合传统相关运算的峰值旁瓣比与感知哈希算法客观度量所跟目标, 对遮挡和形变等复杂情况进行高置信度判断, 进而自适应的选择模型更新率, 克服模型漂移问题; 另外, 利用尺度池算法解决跟踪中的尺度估计问题, 进一步提高了算法的稳健性。通过OTB-2015数据集测试表明: 提出的HCF算法能精准判别出由于遮挡形变等情况导致的无效跟踪, 相比于当前主流的鲁棒性跟踪算法, 具有更优秀的性能和表现。本文的创新工作为跟踪领域中的目标准确度判别问题提供了新的思路。

To prevent over-deformation and to solve occlusion problems that are difficult to solve for correlated filtering tracking algorithms in the tracking field, an improved multiscale target tracking algorithm based on PSR and objective similarity was proposed in this paper. The proposed method combined a traditional correlation operation, peak side lobe ratio, with a perceptual hashing algorithm to tackle problems such as target occlusion, over deformation, and other complex scene judgments. Experimental results using the OTB-2015 demonstrate proposed algorithm's reliability and integrity of the target trajectory. The accuracy and robustness of our algorithm is better than that of Kernelized Correlation Filter (KCF) tracking algorithms. This paper presents a novel idea for occlusion detection in the target tracking field.