Multi-target tracking via hierarchical association learning

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• 作者：Songhao Zhu ^{zhush@njupt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chengjian SunAuthor Vitae ; Zhe ShiAuthor Vitae
• 关键词：Hierarchical correlation ; Adaboost algorithm ; Online discriminant analysis apparent model ; Hungarian algorithm ; Continuous energy minimization
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：208
• 期：Complete
• 页码：365-372
• 全文大小：11388 K

文摘

To deal with the issue of multi-target tracking, this paper proposes a hierarchical correlation multi-target tracking trajectory generation method. On the basis of target detection results, the AdaBoost algorithm combined with online discriminant analysis apparent model is first utilized to achieve initial moving object tracking trajectories; then, the Hungarian algorithm is utilized to optimize fragmented and discontinuous tracking trajectories to achieve stable and accurate trajectories fragments; finally, energy minimization based intelligent extrapolation algorithm is utilized to achieve final smoother and continuous tracking trajectories. Experimental results on PETS 2009/2010 benchmark and TUD-Stadtmitte video database demonstrate the effectiveness and efficiency of the proposed method.