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The use of gait for person identification has important advantages such asbeing non-invasive, unobtrusive, not requiring cooperation and being lesslikely to be obscured compared to other biometrics. Existing methods for gaitrecognition require cooperative gait scenarios, in which a single person iswalking multiple times in a straight line in front of a camera. We aim toaddress the challenges of real-world scenarios in which camera feeds capturemultiple people, who in most cases pass in front of the camera only once. Weaddress privacy concerns by using only motion information of walkingindividuals, with no identifiable appearance-based information. As such, wepropose a novel weakly supervised learning framework, WildGait, which consistsof training a Spatio-Temporal Graph Convolutional Network on a large number ofautomatically annotated skeleton sequences obtained from raw, real-world,surveillance streams to learn useful gait signatures. We collected the trainingdata and compiled the largest dataset of walking skeletons called UncooperativeWild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. Werelease the dataset for public use. Our results show that, with fine-tuning, wesurpass the current state-of-the-art pose-based gait recognition solutions. Ourproposed method is reliable in training gait recognition methods inunconstrained environments, especially in settings with scarce amounts ofannotated data.

WildGait: Learning Gait Representations from Raw Surveillance Streams