利用最佳伙伴相似性的改进空间正则化判别相关滤波目标跟踪

摘要

Aiming at the failure of tracking via spatially regularized discriminant correlation filter (SRDCF)algorithm caused by occlusion,scale change and deformation,an improved SRDCF algo-rithm based on Best-Buddies Similarity was proposed.Firstly,the proposed algorithm based on SRD-CF,locating target and estimating scale in the process of object tracking were complemented by using bi-level search strategy.Secondly,a novel robust template matching technique was used to estimate the candidate object position by integrating the spatial weights,the correlation filter score and the Best-Buddies Similarity score,thus the problem of target relocation in the occlusion was resolved.Fi-nally,the adaptive template updating strategy was employed to mitigate the template drift problem in the case of occlusion.The performance of the proposed algorithm was evaluated on OTB-2013 datasets and was compared with 34 popular algorithms.The results show that the accuracy and the success rate of the proposed algorithm are 0.853 and 0.648,w hich are 1.79% and 3.51% higher than the tra-ditional SRDCF algorithm,respectively.The proposed algorithm can deal with the matter of occlu-sion,scale change and deformation effectively,and has some value of research.%针对空间正则化判别相关滤波跟踪算法(SRDCF)在目标发生遮挡、尺度变化和形变情况下的跟踪失败问题,提出利用最佳伙伴相似性的改进SRDCF目标跟踪算法.首先,以SRDCF算法为基础,利用双层搜索策略解决目标跟踪中的目标定位问题和尺度估计问题;然后,利用一种新颖的鲁棒模板匹配技术,通过融合空间权重、相关滤波得分和最佳伙伴相似性得分来估计候选目标位置,解决遮挡情况下的目标重定位问题;最后,采用自适应模板更新策略解决遮挡情况下模板漂移问题.本文采用OTB-2013数据集评估本文算法的性能,同时与34种流行算法进行比较,结果表明本文算法的精确度得分和成功率得分分别为0.853和0.648,相比传统的SRDCF算法分别提高1.79% 和3.51%.本文算法能很好地解决目标遮挡、尺度变化和形变情况下的目标跟踪问题,具有一定研究价值.