GaitSet：基于深度集的跨视角步态识别
GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
摘要：
步态是一种可以远距离识别的独特生物特征，因此，它在预防犯罪、法医鉴定和社会安全方面有着广泛的应用。为了描述步态，现有的步态识别方法要么利用步态模板，这使得它很难保持时间信息，或步态序列，保持不必要的顺序约束，从而失去了步态识别的灵活性。在本文中，我们提出了一种新的观点，即利用步态作为深度集，这意味着一组步态帧由全局-局部融合深度网络集成，其灵感来自我们的左半球和右半球处理信息以学习可用于识别的信息的方式。基于这种深集视角，我们的方法不受帧排列的影响，并且可以自然地整合来自不同场景下获取的不同视频的帧，例如不同的视角，不同的衣服或不同的物品携带条件。实验表明，在正常行走条件下，我们的单模型方法在CASIA-B步态数据集上的平均rank-1准确率为96.1%，在OU-MVLP步态数据集上的准确率为87.9%。在各种复杂的场景下，我们的模型也表现出很高的鲁棒性。它实现了90.8%和70.3%的准确率CASIA-B下，背着包和穿着外套的步行条件下，分别显着优于现有的最好的方法。此外，所提出的方法保持了令人满意的准确性，即使在测试样本中只有少量的帧，例如，它达到了85.0%的CASIA-B，即使只使用7帧。源代码已在https://github.com/AbnerHqC/GaitSet上发布。
英文摘要：
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait template which makes it difficult to preserve temporal information, or a gait sequence that maintains unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification. Based on this deep set perspective, our method is immune to frame permutations, and can naturally integrate frames from different videos that have been acquired under different scenarios, such as diverse viewing angles, different clothes, or different item-carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset. Under various complex scenarios, our model also exhibits a high level of robustness. It achieves accuracies of 90.8% and 70.3% on CASIA-B under bag-carrying and coat-wearing walking conditions respectively, significantly outperforming the best existing methods. Moreover, the proposed method maintains a satisfactory accuracy even when only small numbers of frames are available in the test samples; for example, it achieves 85.0% on CASIA-B even when using only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
