Human action tracking guided by key-frames

The model-based approaches for tracking of human bodies in image sequences can be categorised into two types; fitting model to body frame by frame, and accumulating estimated pose displacements in successive frames after model fitting at the initial frame. The latter has an inherent drawback as accumulation of tracking errors while the one has a great advantage as small computational efforts compared with the former. This paper proposes a new method which can correct the tracking errors by propagation from fitting model to body at a few key-frames. The propagation makes it possible to establish tracking of bodies under occlusion. Capturing the actor's motions in real old movies is presented.

[1]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Pertti Roivainen,et al.  3-D Motion Estimation in Model-Based Facial Image Coding , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  K. Rohr.  Towards model-based recognition of human movements in image sequences , 1994 .

[4]  Jitendra Malik,et al.  Tracking people with twists and exponential maps , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[5]  J. O'Rourke,et al.  Model-based image analysis of human motion using constraint propagation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[7]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Michael J. Black,et al.  Cardboard people: a parameterized model of articulated image motion , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[9]  Takuya Kondo,et al.  Incremental tracking of human actions from multiple views , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[10]  Reinhard Koch,et al.  Dynamic 3-D Scene Analysis Through Synthesis Feedback Control , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Masanobu Yamamoto,et al.  Human motion analysis based on a robot arm model , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Fumio Kishino,et al.  Human posture estimation from multiple images using genetic algorithm , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[13]  Pietro Perona,et al.  Monocular tracking of the human arm in 3D , 1995, Proceedings of IEEE International Conference on Computer Vision.

[14]  Ioannis A. Kakadiaris,et al.  Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  James M. Rehg,et al.  Digiteyes: Vision-based Human Hand Tracking Contents 1 Introduction 2 2 the Articulated Mechanism Tracking Problem 2 3 State Model for Articulated Mechanisms 4 , 1993 .

[16]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.