Global Motion Model for Stereovision-Based Motion Analysis
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Global Motion Model for Stereovision-Based Motion Analysis Jia Wang,1 Zhencheng Hu,2 Keiichi Uchimura,2 and Hanqing Lu1 1 National
Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100080, China 2 Department of Computer Science, Faculty of Engineering, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan Received 26 October 2005; Revised 11 January 2006; Accepted 21 January 2006 Recommended for Publication by Dimitrios Tzovaras An advantage of stereovision-based motion analysis is that the depth information is available, thus motion can be estimated more precisely in 2.5D stereo coordinate system (SCS) constructed by the depth and the image coordinates. In this paper, stereo global motion in SCS, which is induced by 3D camera motion in real-world coordinate system (WCS), is parameterized by a fiveparameter global motion model (GMM). Based on such model, global motion can be estimated and identified directly in SCS without knowing the physical parameters about camera motion and camera setup in WCS. The reconstructed global motion field accords with the spatial structure of the scene much better. Experiments on both synthetic data and real-world images illustrate its promising performance. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
The advantage of stereovision-based motion analysis is that the depth/disparity information can be computed. Considering the depth information together with the image coordinates, motion can be analyzed more precisely in a 2.5D space rather than the traditional 2D image plane. This paper, by expressing the 2.5D space as stereo coordinate system (SCS), addresses the problem of global motion modeling in SCS. Global motion model (GMM) is commonly used to describe the effect of camera motion (global motion) acting on video image. By GMM, global motion can be distinguished from image motion induced by moving objects (local motion), thus moving objects can be extracted from the image. In the literature, single-camera-based GMM approaches [1–3], which analyze the camera motion based on 2D imagespace shifts [1], cannot describe the global motion accurately when the depth of field is great. By using stereovision, global motion can be estimated more precisely from 2.5D stereomotion analysis using the depth and image coordinates. The reconstructed global motion field will accord with the spatial structure of the scene much better, which makes moving object’s detection much easier. In this paper, a five-parameter stereo GMM is proposed to parameterize global motion in SCS based on the analysis of 3D camera motion. Different from the previous works aiming to recover the physical parameters of camera motion in real-world coordinate system (WCS) [4–8], the presented
model pays more attention to the fast distinguishing of global motion and local motion directly from stereo data. Thus instead of estimating the real camera motion in WCS, global motion is estimated and identified di
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