A New Model-Based Method for Multi-View Human Body Tracking and Its Application to View Transfer in Image-Based Rendering

• Published in: IEEE transactions on multimedia Vol. 20; no. 6; pp. 1321 - 1334
• Main Authors: Liu, Zhong, Lin, Zhouchi, Wei, Xiguang, Chan, Shing-Chow
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2018
• Subjects: Biological system modeling; Cameras; Deformable models; fly-over view transfer; free-form deformation artifacts suppression; Human body tracking; model-based and image-based rendering; Rendering (computer graphics); Solid modeling; textured deformable model; Three-dimensional displays; Tracking
• DOI: 10.1109/TMM.2017.2767781
• ISSN: 1520-9210

This paper proposes a new multi-view human body tracking and model-based rendering system with a textured deformable human body model and explores its practical application to view transfer in image-based rendering (IBR). The proposed approach first reconstructs an initial 3-D model of the human subject offline using Kinect depth cameras or from a general 3-D model if such information is unavailable in the original data. The human pose of the subject is then tracked with multi-view videos using an annealed particle filter (APF)-based tracker with a new color-based likelihood function and a Bayesian-Kalman filter smoother. The previous captured model can then be deformed to the new position for model-based rendering. An immediate application of the proposed approach is to support fly-over effects for view transfer in IBR systems with limited cameras. It avoids the reconstruction of the complete dynamic 3-D model where a large number of cameras may be required. Moreover, for static background, the background can be rendered using IBR with precaptured depth maps to further enhance the user's experience. To reduce the artifacts during fly-over caused by tracking errors and model deformation, a novel morphing technique utilizing a new free-form deformation-based artifacts suppression (FFD-AS) method and other user interface design techniques are also proposed. It allows smooth transition between the original view and the model-rendered views. The performance of the proposed algorithm is evaluated using the publicly available HumanEva dataset and our captured RGB-D multi-view dataset. Experimental results show that the proposed APF-based tracker offered improved tracking performance compared with the conventional bidirectional silhouette likelihood criterion. The proposed morphing approach is also shown to be effective in mitigating the rendering artifacts during view transfer.