A New Multi-View Articulated Human Motion Tracking Algorithm With Improved Silhouette Extraction and View Adaptive Fusion

This paper proposes a new articulated human motion tracking and pose estimation algorithm using an improved silhouette extraction method with view adaptive fusion. It is developed around the baseline algorithm in HumanEva, which uses the Annealed Particle Filter (APF). Shadow detection and removal and a level-set method are employed to achieve better silhouette extraction. An adaptive view fusion approach is also proposed to improve the matching between the human 3D model and the observations. Experimental results show that the proposed approach has considerably better performance than the baseline algorithm in the HumanEva dataset, due to better shadow handling and data fusion of multiple views.