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An Efficient Edge Artificial Intelligence MultiPedestrian Tracking Method With Rank Constraint

机译:具有等级约束的高效边缘人工智能多行人跟踪方法

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摘要

Characterized by the ability to handle varying number of objects, tracking by detection framework becomes increasingly popular in multiobject tracking (MOT) problem. However, the tracking performance heavily depends on the object detector. Considering that data association optimization and association affinity model are two key parts in MOT, an online multipedestrian tracking method is proposed to formulate a more effective association affinity model. It includes a two-step data association taking advantage of rank-based dynamic motion affinity model. The rank-based dynamic motion affinity model is used to estimate the object state and refine the trajectory for each of target to achieve the noiseless trajectory. Both strategies are beneficial to eliminate ambiguous detection responses during association. To fairly verify the proposed method, three public datasets are adopted. Both qualitative and quantitative experiment results demonstrate the superiorities of the proposed tracking algorithm in comparison with its counterparts.
机译:以处理不同数量对象的能力为特征,通过检测框架进行跟踪在多对象跟踪(MOT)问题中变得越来越流行。但是,跟踪性能在很大程度上取决于对象检测器。考虑到数据关联优化和关联亲和度模型是MOT中的两个关键部分,提出了一种在线多行人跟踪方法来建立更有效的关联亲和度模型。它包括利用基于排名的动态运动亲和力模型的两步数据关联。基于等级的动态运动亲和力模型用于估计对象状态并细化每个目标的轨迹,以实现无噪声的轨迹。两种策略都有利于消除关联期间的歧义检测响应。为了公平地验证所提出的方法,采用了三个公共数据集。定性和定量实验结果均证明了该跟踪算法与其同类算法相比的优越性。

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