A Real-Time Model-Based Human Motion Tracking and Analysis for Human-Computer Interface Systems
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A Real-Time Model-Based Human Motion Tracking and Analysis for Human Computer Interface Systems Chung-Lin Huang Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu 30055, Taiwan Email: [email protected]
Chia-Ying Chung Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu 30055, Taiwan Email: [email protected] Received 3 June 2002; Revised 10 October 2003 This paper introduces a real-time model-based human motion tracking and analysis method for human computer interface (HCI). This method tracks and analyzes the human motion from two orthogonal views without using any markers. The motion parameters are estimated by pattern matching between the extracted human silhouette and the human model. First, the human silhouette is extracted and then the body definition parameters (BDPs) can be obtained. Second, the body animation parameters (BAPs) are estimated by a hierarchical tritree overlapping searching algorithm. To verify the performance of our method, we demonstrate different human posture sequences and use hidden Markov model (HMM) for posture recognition testing. Keywords and phrases: human computer interface system, real-time vision system, model-based human motion analysis, body definition parameters, body animation parameters.
1. INTRODUCTION Human motion tracking and analysis has a lot of applications, such as surveillance systems and human computer interface (HCI) systems. A vision-based HCI system need to locate and understand the user’s intention or action in real time by using the CCD camera input. Human motion is a highly complex articulated motion. The inherent nonrigidity of human motion coupled with the shape variation and self-occlusions make the detection and tracking of human motion a challenging research topic. This paper presents a framework for tracking and analyzing human motion with the following aspects: (a) real-time operation, (b) no markers on the human object, (c) near-unconstrained human motion, and (d) data coordination from two views. There are two typical approaches to human motion analysis: model based and nonmodel based, depending on whether predefined shape models are used. In both approaches, the representation of the human body has been developed from stick figures [1, 2], 2D contour [3, 4], and 3D volumes [5, 6] with increasing complexity of the model. The stick figure representation is based on the observation that human motions of body parts result from the movement of the relative bones. The 2D contour is allied with the projec-
tion of 3D human body on 2D images. The 3D volumes, such as generalized cones, elliptical cylinders [7], spheres [5], and blobs [6] describe human model more precisely. With no predefined shape models, heuristic assumptions, which impose constraints on feature correspondence and decreasing search space, are usually used to establish the correspondence of joints between successive frames. Moeslund and Granum [8] give an extensive survey of computer visionbased human motion capture. Most of the ap
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