Real-time gait recognition system based on lightweight dual-modal pose estimation - Aodi, Din; Shiping, Ye; Nedzved, Alexander M.; Anosov, Victor S. (Journal of the Belarusian State University. Mathematics and Informatics, 2026)
A lightweight approach for joint 2D and 3D gait analysis is proposed. A dual-modal gait recognition system is adopted, achieving high-precision real-time analysis through a 2D and 3D collaborative estimation architecture. The system employs MediaPipe for real-time 2D keypoint detection and gait metric extraction. An improved lightweight architecture MTSA-former with 2.5 mln parameters is used for 3D pose estimation. This model utilises cascaded processing incorporating temporal and spatial modules, along with an adjacency matrix based on skeletal topological structure to model physical constraints between joints. A local-global hybrid spatial modelling strategy fusing graph convolutional networks is implemented to achieve efficient 3D pose estimation, attaining high performance while maintaining lightweight characteristics. This approach ensures accurate and efficient 3D pose reconstruction in real time.