Learning to associate: HybridBoosted multi-target tracker for crowded scene
Learning to associate: HybridBoosted multi-target tracker for crowded scene
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DOI：
10.1109/cvpr.2009.5206735
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Yuan Li;Chang Huang;R. Nevatia
中科院分区：
文献类型：
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作者：
Yuan Li;Chang Huang;R. Nevatia
我们提出了一种基于学习的分层方法，从一个单一的摄像头多目标跟踪逐步关联检测响应到越来越长的跟踪片段（tracklets），最后所需的目标轨迹。为了定义tracklet关联的亲和力，大多数以前的工作依赖于选择性选择的参数模型，而我们的方法是能够自动选择各种功能和相应的非参数模型，并将它们联合收割机，以最大限度地提高训练数据的鉴别力凭借HybridBoost算法。在该算法中使用的混合损失函数，因为tracklet的关联被制定为一个联合的问题的排名和分类：排名部分的目的是排名正确的tracklet关联高于其他替代品，分类部分负责拒绝错误的关联时，没有进一步的关联应该做。通过在具有挑战性的数据集中跟踪行人进行实验。我们将我们的方法与最先进的算法进行比较，以显示其在跟踪精度方面的改进。
We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragments (tracklets) and finally the desired target trajectories. To define tracklet affinity for association, most previous work relies on heuristically selected parametric models; while our approach is able to automatically select among various features and corresponding non-parametric models, and combine them to maximize the discriminative power on training data by virtue of a HybridBoost algorithm. A hybrid loss function is used in this algorithm because the association of tracklet is formulated as a joint problem of ranking and classification: the ranking part aims to rank correct tracklet associations higher than other alternatives; the classification part is responsible to reject wrong associations when no further association should be done. Experiments are carried out by tracking pedestrians in challenging datasets. We compare our approach with state-of-the-art algorithms to show its improvement in terms of tracking accuracy.