Abstract
Human gait analysis presents an opportunity to study complex spatiotemporal data transpiring as co-movement patterns of multiple moving objects (i.e., human joints). Such patterns are acknowledged as movement signatures specific to an individual, offering the possibility to identify each individual based on unique gait patterns. We present a spatiotemporal deep learning model, dubbed ST-DeepGait, to featurize spatiotemporal co-movement patterns of human joints, and accordingly classify such patterns to enable human gait recognition. To this end, the ST-DeepGait model architecture is designed according to the spatiotemporal human skeletal graph in order to impose learning the salient local spatial dynamics of gait as they occur over time. Moreover, we employ a multi-layer RNN architecture to induce a sequential notion of gait cycles in the model. Our experimental results show that ST-DeepGait can achieve recognition accuracy rates over 90%. Furthermore, we qualitatively evaluate the model with the class embeddings to show interpretable separability of the features in geometric latent space. Finally, to evaluate the generalizability of our proposed model, we perform a zero-shot detection on 10 classes of data completely unseen during training and achieve a recognition accuracy rate of 88% overall. With this paper, we also contribute our gait dataset captured with an RGB-D sensor containing approximately 30 video samples of each subject for 100 subjects totaling 3087 samples. While we use human gait analysis as a motivating application to evaluate ST-DeepGait, we believe that this model can be simply adopted and adapted to study co-movement patterns of multiple moving objects in other applications such as in sports analytics and traffic pattern analysis.