结合SAMF和视觉显著性的目标跟踪算法  

An Object Tracking Algorithm Combining SAMF with Visual Saliency

作　　者：余汉蓉 林彬[1] 俞增林 YU Hanrong;LIN Bin;YU Zenglin(School of Science,Guilin University of Technology,Guilin 541004,China)

机构地区：[1]桂林理工大学理学院,广西桂林541004

出　　处：《电光与控制》2021年第1期15-18,32,共5页Electronics Optics & Control

基　　金：国家自然科学基金(61703117);国家级2019年大学生创新创业训练计划支持项目(201910596049);广西科技计划项目(2019GXNSFBA245056);广西高校中青年教师基础能力提升项目(2017KY0260)。

摘　　要：为提高SAMF算法在复杂场景下的跟踪性能,提出了一种结合SAMF和视觉显著性的目标跟踪算法。在SAMF相关滤波跟踪框架的基础上,通过设计置信度判别策略评价SAMF跟踪结果的可靠性,当认定跟踪结果为低置信度时启用显著性检测算法对其进行修正,从而实现目标的重定位以解决遮挡等因素导致的跟踪漂移问题。实验表明,所提出的改进算法极大地提升了SAMF算法的总体性能和应对多种不同干扰因素时的跟踪鲁棒性,同时保持了良好的跟踪实时性。In order to improve the tracking performance of the SAMF algorithm in complex scenarios,an object tracking algorithm combining SAMF with visual saliency is proposed.Based on the SAMF correlation filter tracking framework,the reliability of the SAMF tracking results is evaluated by designing a confidence discrimination strategy.When the tracking results are identified as low confidence,the saliency detection algorithm is used to correct them,so as to realize the target’s relocation to address the tracking drift caused by occlusion and other factors.Experiment results show that the proposed algorithm greatly improves the overall performance and tracking robustness of the SAMF algorithm when dealing with various interference factors,while maintaining good real-time performance.

关 键 词：目标跟踪 相关滤波 跟踪置信度 显著性检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]