融合相似性检测的抗遮挡粒子滤波跟踪算法  

Anti-Occlusion Particle Filter Tracking Algorithm Based on Similarity Detection

作　　者：邓利平[1] 肖何[1] 王娟[1] DENG Liping;XIAO He;WANG Juan(School of Computer Science,China West Normal University,Nanchong,Sichuan 637002,China)

机构地区：[1]西华师范大学计算机学院,四川南充637002

出　　处：《计算机工程与应用》2022年第14期185-193,共9页Computer Engineering and Applications

基　　金：四川省自然科学基金面上项目(2019YJ0342);四川省科技计划资助(21YYJC1819);四川省教育厅一般项目(14ZB0141)。

摘　　要：在粒子滤波跟踪算法运行过程中,由于目标遮挡导致丢失目标,将严重地降低跟踪精度与鲁棒性。为了解决此问题,提出了目标丢失状态判定方法和基于改进序贯相似性检测的目标位置重建方法,当检测到目标丢失时,重启跟踪算法。改进序贯相似性检测使用Bhattacharyya距离代替像素累积误差,更好地适应检测目标发生旋转、形变、缩放等情况。使用OTB-100标准数据集,将该算法和传统粒子滤波跟踪算法、SCM等经典算法比较。实验结果表明,对于含遮挡特性视频序列,本文算法比传统粒子滤波跟踪算法和OTB-100抗遮挡最优算法跟踪成功率分别提高36.6%和3.2%,提升了跟踪过程的稳定性。此外,还将实验结果与最新粒子滤波跟踪研究成果作对比分析。When running particle filter tracking algorithm,the loss of target due to occlusion will seriously reduce track-ing accuracy and robustness.To solve this problem,a method to determine the state of target loss and a method of target position reconstruction based on improved sequential similarity detection are proposed.When the target loss is detected,the tracking algorithm is restarted.The improved sequential similarity detection uses the Bhattacharyya distance instead of pixel cumulative error,which can better adapt to the situation of target rotation,deformation,scale variation and so on.Using OTB-100 standard datasets,the proposed algorithm is compared with traditional particle filter tracking algorithm,SCM and other classical algorithms.The experimental results show that the tracking success rate of proposed algorithm is 36.6%and 3.2%higher than that of traditional particle filter tracking algorithm and the best anti-occlusion algorithm of OTB-100 datasets,respectively.The proposed algorithm improves the stability of the tracking process.In addition,the experi-mental results are compared with the latest research results of particle filter tracking algorithm.

关 键 词：目标跟踪 粒子滤波 序贯相似性检测 抗遮挡 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]