基于抗遮挡目标模型的跟踪算法综述  

Tracking Algorithms Based on Antiocclusion Object Models

作　　者：谢郭蓉 曲毅[2] 蒋镕圻 Xie Guorong;Qu Yi;Jiang Rongqi(Postgraduate Brigade,Engineering University of PAP,Xi’an,Shaanxi 710086,China;School of Information Engineering,Engineering University of PAP,Xi’an,Shaanxi 710086,China)

机构地区：[1]武警工程大学研究生大队,陕西西安710086 [2]武警工程大学信息工程学院,陕西西安710086

出　　处：《激光与光电子学进展》2022年第8期315-328,共14页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(No.61101238)。

摘　　要：遮挡问题是导致目标跟踪任务失败的重要因素,如何提升算法的抗遮挡性能是跟踪领域的研究热点。本文首先剖析了遮挡易导致跟踪失败的原因,论述了构建强判别性的鲁棒目标模型对提高跟踪算法抗遮挡性能的重要意义,分析了抗遮挡目标模型的构建方案。其次依据目标模型利用的信息类型,将代表性抗遮挡性能较优的算法分为基于有效特征信息、状态估计信息与稳定时空信息三类。而后详尽分析了基于卡尔曼滤波、粒子滤波、局部空间信息、时间上下文信息、时空上下文信息跟踪算法的抗遮挡思路方案、适用遮挡场景、优缺点及改进方案。最后通过不同类型算法在遮挡场景下的跟踪性能比较,对目标模型构建方案抗遮挡的有效性提出思考与分析,并指出学习语义信息轻量化网络设计、场景上下文预测、仿生视觉机理的应用发展方向。Occlusion is an essential factor that often leads to the failure of object tracking.Improving antiocclusion performance of the algorithm has been a research hotspot in tracking.First,this paper analyzes why occlusion easily leads to tracking failure.Furthermore,the importance of constructing a strong discriminant and robust object model to improve the antiocclusion performance of the tracking algorithm and an effective scheme to improve the antiocclusion performance of the target model are discussed.Then,based on the utilization information type of constructing object model,the representative methods with better antiocclusion performance are divided into three categories on the basis of effective feature,state estimation,and stable spatiotemporal informations.Further,the antiocclusion idea scheme,suitable occlusion scene,pros and cons,and improvement schemes of object tracking algorithm based on Kalman filter,particle filter,local spatial information,time context information,and spatiotemporal context information are analyzed in detail.Finally,through performance comparison with the tracking performance of different types of methods in occlusion scenarios,the antiocclusion effectiveness of the object model construction scheme is analyzed.The application and development direction of learning semantic information lightweight network design,scene context prediction,and bionic vision mechanism are presented.

关 键 词：机器视觉 目标跟踪 抗遮挡 状态估计信息 时空上下文 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]