解决步态识别中遮挡问题，同时保持完整步态识别准确率。
Mind the Gap: Bridging Occlusion in Gait Recognition via Residual Gap Correction
- 将遮挡步态视为完整步态的残差偏差，用残差修正方法建模。
- 在Gait3D、GREW、BRIAR数据集上，遮挡场景识别率显著提升。
- 无需成对遮挡/完整数据，适合真实场景应用，兼顾完整步态识别性能。
步态识别因可在远距离识别人体而日益流行，但现有方法大多忽略实际中常见的遮挡问题。部分处理遮挡的方法依赖成对的遮挡与完整步态序列，难以在真实世界获取；且这些方法在完整输入上性能下降。为此，我们提出RG-Gait，一种针对遮挡步态识别的残差修正方法，兼具完整步态保留能力。将遮挡步态视为完整步态表示的残差偏差，网络自适应融合学习到的残差，显著提升遮挡序列识别性能，同时不损失完整输入的准确率。我们在Gait3D、GREW和BRIAR三个挑战性数据集上验证了该方法的有效性。代码已公开于https://github.com/Ayush-00/rg-gait。
原文摘要 · Abstract (English)
Gait is becoming popular as a method of person re-identification because of its ability to identify people at a distance. However, most current works in gait recognition do not address the practical problem of occlusions. Among those which do, some require paired tuples of occluded and holistic sequences, which are impractical to collect in the real world. Further, these approaches work on occlusions but fail to retain performance on holistic inputs. To address these challenges, we propose RG-Gait, a method for residual correction for occluded gait recognition with holistic retention. We model the problem as a residual learning task, conceptualizing the occluded gait signature as a residual deviation from the holistic gait representation. Our proposed network adaptively integrates the learned residual, significantly improving performance on occluded gait sequences without compromising the holistic recognition accuracy. We evaluate our approach on the challenging Gait3D, GREW and BRIAR datasets and show that learning the residual can be an effective technique to tackle occluded gait recognition with holistic retention. We release our code publicly at https://github.com/Ayush-00/rg-gait.
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