Human gait is considered a unique biometric identifier which can be acquired in a covert manner at a distance. However, models trained on existing public domain gait datasets which are captured in controlled scenarios lead to drastic performance decline when applied to real-world unconstrained gait data. On the other hand, video person re-identification techniques have achieved promising performance on large-scale publicly available datasets. Given the diversity of clothing characteristics, clothing cue is not reliable for person recognition in general. So, it is actually not clear why the state-of-the-art person re-identification methods work as well as they do. In this paper, we construct a new gait dataset by extracting silhouettes from an existing video person re-identification challenge which consists of 1,404 persons walking in an unconstrained manner. Based on this dataset, a consistent and comparative study between gait recognition and person re-identification can be carried out. Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait. Our results suggest that recognizing people by their gait in real surveillance scenarios is feasible and the underlying gait pattern is probably the true reason why video person re-idenfification works in practice.

翻译：人体步态被认为是一种独特的生物特征标识，可在远距离隐蔽条件下获取。然而，在受控场景中采集的现有公开步态数据集训练的模型，应用于真实无约束步态数据时会导致性能显著下降。另一方面，视频行人重识别技术在大规模公开数据集上已取得显著性能。鉴于衣着特征的多样性，服装线索通常无法可靠用于行人识别。因此，当前最先进的行人重识别方法之所以有效，其根本原因尚不明确。本文通过从现有视频行人重识别挑战数据中提取轮廓，构建了一个包含1404名行人无约束行走场景的新步态数据集。基于该数据集，可对步态识别与行人重识别进行一致性的对比研究。实验结果表明，当前在受控场景数据下设计的步态识别方法不适用于真实监控场景，为此我们提出了一种名为RealGait的新型步态识别方法。研究结果显示，在真实监控场景中通过步态识别行人具有可行性，而步态模式本身可能正是视频行人重识别方法在实践中有效性的真正原因。