Automatic Detection and Tracking of Human Motion with a View-Based Representation

Abstract. This paper proposes a solution for the automatic detection and tracking of human motion in image sequences. Due to the complexity of the human body and its motion, automatic detection of 3D human motion remains an open, and important, problem. Existing approaches for automatic detection and tracking focus on 2D cues and typically exploit object appearance (color distribution, shape) or knowledge of a static background. In contrast, we exploit 2D optical flow information which provides rich descriptive cues, while being independent of object and background appearance. To represent the optical flow patterns of people from arbitrary viewpoints, we develop a novel representation of human motion using low-dimensional spatio-temporal models that are learned using motion capture data of human subjects. In addition to human motion (the foreground) we probabilistically model the motion of generic scenes (the background); these statistical models are defined as Gibbsian fields specifie...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Ronan Fablet, Michael J. Black
• Keywords: Automatic Detection, Computer Vision, ECCV 2002, Human Motion, Motion Capture Data, Motion Observations, Optical Flow Observations