Directly benefiting from the rapid advancement of deep learning methods, person re-identification (Re-ID) applications have been widespread with remarkable successes in recent years. Nevertheless, cross-scene Re-ID is still hindered by large view variation, since it is challenging to effectively exploit and leverage the temporal clues due to heavy computational burden and the difficulty in flexibly incorporating discriminative features. To alleviate, we articulate a long-short temporal–spatial clues excited network (LSTS-NET) for robust person Re-ID across different scenes. In essence, our LSTS-NET comprises a motion appearance model and a motion-refinement aggregating scheme. Of which, the former abstracts temporal clues based on multi-range low-rank analysis both in consecutive frames and in cross-camera videos, which can augment the person-related features with details while suppressing the clutter background across different scenes. In addition, to aggregate the temporal clues with spatial features, the latter is proposed to automatically activate the person-specific features by incorporating personalized motion-refinement layers and several motion-excitation CNN blocks into deep networks, which expedites the extraction and learning of discriminative features from different temporal clues. As a result, our LSTS-NET can robustly distinguish persons across different scenes. To verify the improvement of our LSTS-NET, we conduct extensive experiments and make comprehensive evaluations on 8 widely-recognized public benchmarks. All the experiments confirm that, our LSTS-NET can significantly boost the Re-ID performance of existing deep learning methods, and outperforms the state-of-the-art methods in terms of robustness and accuracy.

中文翻译：

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用于鲁棒人员重新识别的长短时空线索激发网络

直接受益于深度学习方法的快速发展，行人重新识别 (Re-ID) 应用在近年来得到了广泛的应用并取得了显着的成功。然而，跨场景 Re-ID 仍然受到大视图变化的阻碍，因为由于计算负担重和难以灵活结合判别特征，有效地利用和利用时间线索具有挑战性。为了缓解这种情况，我们阐明了一个长短时空线索激发网络（LSTS-NET），用于跨不同场景的鲁棒行人 Re-ID。本质上，我们的 LSTS-NET 包括一个运动外观模型和一个运动细化聚合方案。其中，前者在连续帧和跨摄像机视频中基于多范围低秩分析提取时间线索，它可以通过细节增强与人相关的特征，同时抑制不同场景中的杂乱背景。此外，为了将时间线索与空间特征进行聚合，后者被提出通过将个性化的运动细化层和几个运动激发 CNN 块结合到深层网络中来自动激活特定于个人的特征，从而加快了判别性的提取和学习。来自不同时间线索的特征。因此，我们的 LSTS-NET 可以稳健地区分不同场景中的人。为了验证我们的 LSTS-NET 的改进，我们进行了广泛的实验，并对 8 个广泛认可的公共基准进行了综合评估。所有的实验都证实，我们的 LSTS-NET 可以显着提升现有深度学习方法的 Re-ID 性能，