Multiscale Temporal Network for Video-Based Gait Recognition

Gait is a kind of advanced feature for human identification at a distance. It also contains rich temporal information. In the paper an innovative gait recognition model, Multiscale Temporal Network (MSTN), is designed to extract discriminative feature at multiple scales in the temporal domain. MSTN can build a temporal pyramid from four different temporal resolutions. That means the human body motion can be described from coarse to fine by the four pathways in the network. The method is verified on a popular databset, CASIA-B. The experimental results show that the proposed MSTN can observably improve the recognition rate and MSTN is a straightforward and effective solution. It also shows that there is great potential in gait feature extraction from the temporal domain.