Abstract
Gait recognition aims to identify individuals based on their body shape and walking patterns. Though much progress has been achieved driven by deep learning, gait recognition in real-world surveillance scenarios remains quite challenging to current methods. Conventional approaches, which rely on periodic gait cycles and controlled environments, struggle with the non-periodic and occluded silhouette sequences encountered in the wild. In this paper, we propose a novel framework, TrackletGait, designed to address these challenges in the wild. We propose Random Tracklet Sampling, a generalization of existing sampling methods, which strikes a balance between robustness and representation in capturing diverse walking patterns. Next, we introduce Haar Wavelet-based Downsampling to preserve information during spatial downsampling. Finally, we present a Hardness Exclusion Triplet Loss, designed to exclude low-quality silhouettes by discarding hard triplet samples. TrackletGait achieves state-of-the-art results, with 77.8 and 80.4 rank-1 accuracy on the Gait3D and GREW datasets, respectively, while using only 10.3M backbone parameters. Extensive experiments are also conducted to further investigate the factors affecting gait recognition in the wild.
Abstract (translated)
步态识别旨在通过人体形状和行走模式来识别个体。尽管在深度学习的推动下已取得了显著进展，但在实际监控场景中进行步态识别仍然具有挑战性。传统的依赖于周期性的步行循环和受控环境的方法，在面对非周期性和被遮挡的身体轮廓序列时难以应对。 本文提出了一种新型框架 TrackletGait，旨在解决野外环境中遇到的这些挑战。我们提出了随机片段采样（Random Tracklet Sampling），这是一种现有采样方法的泛化版本，能够在捕捉多样化行走模式的同时保持鲁棒性与表现力之间的平衡。接下来，我们介绍了基于哈尔小波的下采样(Haar Wavelet-based Downsampling)，该技术在空间下采样的过程中能够保存信息。最后，我们提出了一种难度排除三元组损失（Hardness Exclusion Triplet Loss），旨在通过丢弃难以处理的三元组样本来排除低质量的身体轮廓。 TrackletGait 实现了最先进的结果，在 Gait3D 和 GREW 数据集上分别达到了 77.8% 和 80.4% 的首位准确率，同时仅使用了10.3M个骨干参数。此外，还进行了广泛的实验以进一步探讨影响野外步态识别的因素。 通过这些创新性的方法和技术，TrackletGait 能够在实际监控场景中更有效地实现步态识别。
URL
https://arxiv.org/abs/2508.02143
