Abstract
In this paper, a layered deformable model (LDM) is proposed for human body pose recovery in gait analysis. This model is inspired by the manually labeled silhouettes in (Z. Liu, et al., July 2004) and it is designed to closely match them. For fronto-parallel gait, the introduced LDM model defines the body part widths and lengths, the position and the joint angles of human body using 22 parameters. The model consists of four layers and allows for limb deformation. With this model, our objective is to recover its parameters (and thus the human body pose) from automatically extracted silhouettes. LDM recovery algorithm is first developed for manual silhouettes, in order to generate ground truth sequences for comparison and useful statistics regarding the LDM parameters. It is then extended for automatically extracted silhouettes. The proposed methodologies have been tested on 10005 frames from 285 gait sequences captured under various conditions and an average error rate of 7% is achieved for the lower limb joint angles of all the frames, showing great potential for model-based gait recognition
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead
I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.