提出TrackletGait框架，提升真实场景下步态识别的鲁棒性。
TrackletGait: A Robust Framework for Gait Recognition in the Wild
- 采用随机轨迹片段采样，更好捕捉多变行走模式。
- 在降采样中用哈尔小波保留关键信息，减少失真。
- 设计难例排除三元组损失，自动过滤低质量序列。
步态识别旨在通过人体轮廓和行走姿态识别个体。尽管深度学习推动了该领域进展，但真实监控场景中的步态识别仍面临巨大挑战。传统方法依赖周期性步态和受控环境，在野外非周期性、遮挡严重的轮廓序列上表现不佳。本文提出新框架TrackletGait，解决野外复杂条件下的识别难题。首先，提出随机轨迹片段采样，平衡鲁棒性与表征能力，捕捉多样行走模式；其次，引入哈尔小波降采样，有效保留空间下采样中的关键信息；最后，设计难例排除三元组损失，通过剔除困难样本提升训练质量。TrackletGait在Gait3D和GREW数据集上分别取得77.8%和80.4%的rank-1准确率，仅使用10.3M主干参数。大量实验进一步分析了影响野外步态识别的关键因素。
原文摘要 · Abstract (English)
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.
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