UniGait: A Unified Transformer-based Multitask Framework for Gait Analysis in the Wild

Gait recognition is a rapidly emerging and significant area of biometrics, leveraging the unique walking patterns of individuals to perform personal identification and facilitate healthcare monitoring, such as elderly care, fall detection, etc. While existing gait recognition methods perform well in indoor, or short-range environments, their effectiveness diminishes significantly when applied to unconstrained outdoor scenarios. Challenges such as environmental turbulence, occlusion, varying viewing angles contribute to this performance drop. To address these challenges and enhance gait recognition accuracy in real-world settings, while also expanding the functionality of gait features for healthcare applications, we propose a unified multitask framework called UniGait. UniGait is designed to perform a comprehensive range of gait analysis tasks, including gait recognition and estimation of gait-related human attributes. UniGait is built upon a transformer-based architecture, which leverages the power of a cross-attention mechanism to simultaneously process multiple sub-tasks. This multitask learning approach allows the model to extract more robust gait features by jointly learning gait recognition and human attribute estimation, leading to improved overall performance. We report the results of extensive experiments and analysis on large-scale, real-world datasets collected under challenging conditions, including long-range (up to 1000 meters) and high-pitch angles (including UAV-based data). The results demonstrated state-of-the-art performance, highlighting the potential of UniGait for deployment in real-world applications, making it a valuable tool for a range of biometric and healthcare monitoring scenarios.