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首页> 外文期刊>Journal of visual communication & image representation >Weighted correlation filters guidance with spatial-temporal attention for online multi-object tracking
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Weighted correlation filters guidance with spatial-temporal attention for online multi-object tracking

机译：加权相关滤波指导，时空关注在线多目标跟踪

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摘要

In recent years, discriminative correlation filters based trackers have made remarkable achievements for single object tracking, while directly applying these trackers for multi-object tracking may encounter some problem in drifted results caused by occlusion and missing detection from the detector. Thus, we propose a weighted-correlation-filters framework with spatial-temporal attention mechanism for online multi-object tracking to solve the above problems. First, we use the weighted correlation filters with dynamic updating scheme to pre-track each object in the current frame, which helps to filter out the improper detection according to the position of pre-tack for each object and is capable of tracking objects of the false negative. Then, we introduce a spatial-temporal attention mechanism to produce a discriminative appearance model and calculate reliable similarity scores for data association. The proposed online algorithm achieves 48.4% in MOTA on challenging MOT17 benchmark dataset and better performance on MT and ML than some offline methods. (C) 2019 Published by Elsevier Inc.
机译：近年来，基于判别相关滤波器的跟踪器在单对象跟踪方面取得了令人瞩目的成就，而直接将这些跟踪器应用于多对象跟踪可能会因遮挡和检测器检测缺失而导致漂移结果出现问题。因此，我们提出了一种具有时空关注机制的加权相关滤波器框架，用于在线多目标跟踪，以解决上述问题。首先，我们使用具有动态更新方案的加权相关滤波器对当前帧中的每个对象进行预跟踪，这有助于根据每个对象的预定位位置过滤出不正确的检测，并能够跟踪目标对象。假阴性。然后，我们引入了一种时空注意力机制，以产生可辨别的外观模型，并为数据关联计算可靠的相似度得分。所提出的在线算法在具有挑战性的MOT17基准数据集上的MOTA达到48.4％，并且在MT和ML上的性能要优于某些离线方法。 （C）2019由Elsevier Inc.发布

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