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首页> 外文期刊>IEEE transactions on multimedia >Deep Alignment Network Based Multi-Person Tracking With Occlusion and Motion Reasoning
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Deep Alignment Network Based Multi-Person Tracking With Occlusion and Motion Reasoning

机译：基于深度对准网络的具有遮挡和运动推理的多人跟踪

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摘要

Tracking-by-detection is one of the typical paradigms for multi-person tracking, due to the availability of automatic pedestrian detectors. However, existing multi-person trackers are greatly challenged by misalignment in the pedestrian detectors (i.e., excessive background and part missing) and occlusion. To effectively handle these problems, we propose a deep alignment network-based multi-person tracking method with occlusion and motion reasoning. Specifically, the inaccurate detections are first corrected via a deep alignment network, in which an alignment estimation module is used to automatically learn the spatial transformation of these detections. As a result, the deep features from our alignment network will have better representation power and, thus, lead to more consistent tracks. Then, a coarse-to-fine schema is designed for construing a discriminative association cost matrix with spatial, motion, and appearance information. Meanwhile, a principled approach is developed to allowour method to handle occlusion with motion reasoning and the reidentification ability of the pedestrian alignment network. Finally, the association problem is solved via a simple yet real-time Hungarian algorithm. Comprehensive experiments on MOT16, ISSIA soccer, PETS09, and TUD datasets validate the effectiveness and robustness of our proposed tracker.
机译：由于具有自动行人检测器，因此按检测跟踪是多人跟踪的典型范例之一。然而，现有的多人跟踪器受到行人检测器的未对准（即，过多的背景和部分丢失）和遮挡的挑战。为了有效地解决这些问题，我们提出了一种基于深度对准网络的，具有遮挡和运动推理的多人跟踪方法。具体而言，首先通过深度对齐网络对不准确的检测进行校正，在该网络中，使用对齐估计模块自动了解这些检测的空间变换。结果，来自对准网络的深层特征将具有更好的表示能力，并因此导致更一致的轨迹。然后，设计了一种从粗到精的方案，以构造具有空间，运动和外观信息的区分性关联成本矩阵。同时，开发了一种原则性的方法来允许我们的方法利用运动推理和行人对准网络的重新识别能力来处理遮挡。最后，通过简单但实时的匈牙利算法解决了关联问题。在MOT16，ISSIA足球，PETS09和TUD数据集上进行的全面实验验证了我们提出的跟踪器的有效性和鲁棒性。

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