卷积特征多伯努利视频多目标跟踪算法  

Visual Multi-Object Tracking Using Convolution Feature and Multi-Bernoulli Filter

作　　者：杨金龙[1] 汤玉 张光南 YANG Jinlong;TANG Yu;ZHANG Guangnan(School of Internet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China;School of Information Engineering,Chang'University,Xi'an 710064,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]长安大学信息工程学院,西安710064

出　　处：《计算机科学与探索》2019年第11期1945-1957,共13页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金Nos.61305017,61772237;江苏省自然科学基金No.BK20181340~~

摘　　要：基于随机有限集理论的多伯努利滤波方法能够有效处理多目标跟踪中数目未知且时变的问题,但难以适应复杂环境下视频多目标跟踪中目标之间或背景等干扰问题,尤其是目标相互紧邻和被遮挡时,会导致跟踪精度下降,甚至目标漏跟。针对该问题,在多伯努利滤波框架下,深度分析目标的特征信息,引入抗干扰的卷积特征,提出基于卷积特征的多伯努利视频多目标跟踪算法,并在目标状态提取过程中,进一步提出模板更新,使用自适应学习速率进行更新,适应目标的变化,以解决目标紧邻相互干扰的问题。最后,引入粒子标记技术,实现对视频多目标的航迹跟踪。实验结果表明,提出算法能够有效区分复杂环境下的紧邻多目标,且具有较好的跟踪精度。Multi-Bernoulli(MB)filter based on stochastic finite set theory has been demonstrated as a promising algorithm for tracking multiple targets with unknown and time-varying number of objects.However,for visual multi-object tracking(VMOT)in a complex scenario,it is difficult to solve the interference between close targets and background clutter.Especially,when the targets are close to each other and occluded,it will lead to the decrease of tracking accuracy and even target missed tracking.To solve these problems,under the framework of multiBernoulli(MB)filter,deeply analyzing the feature information of the target and introducing the anti-interference convolution feature,this paper proposes an effective VMOT algorithm by integrating convolution features into the framework of MB filter.Moreover,in the process of object state extraction,the adaptive scheme of template update is proposed by using an adaptive learning rate,which makes the template adapt to the scale variation and handle the problem of closely-spaced object tracking effectively.Finally,the particle labelling technique is employed to realize visual multi-object tracking.Experimental results show that,the proposed algorithm can effectively distinguish the closely-spaced object in a complex scenario,and has a high tracking accuracy.

关 键 词：多伯努利滤波 卷积特征 自适应学习 视频多目标跟踪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]