3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter

Sensors

Fakhreddine Ababsa¹  Marouane Boui²  Hicham Hadj-Abdelkader² 

[1] Arts et Métiers Institue of Technology, LISPEN, HESAM University, 75005 Chalon-sur-Saône, France;IBISC Laboratory, University of Evry, 91000 Evry-Courcouronnes, France;

关键词: human tracking;    omnidirectional camera;    ego motion;    particle filter;   

DOI  :  10.3390/s20236985

来源: DOAJ

【 摘 要 】

The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the literature on RGB images acquired from conventional perspective cameras, while omnidirectional images have seldom been used and published research works in this field remains limited. In this study, the Riemannian varieties was considered in order to compute the gradient on spherical images and generate a robust descriptor used along with an SVM classifier for human detection. Original likelihood functions associated with the particle filter are proposed, using both geodesic distances and overlapping regions between the silhouette detected in the images and the projected 3D human model. Our approach was experimentally evaluated on real data and showed favorable results compared to machine learning based techniques about the 3D pose accuracy. Thus, the Root Mean Square Error (RMSE) was measured by comparing estimated 3D poses and truth data, resulting in a mean error of 0.065 m when walking action was applied.

【 授权许可】

Unknown