Abstract
This paper presents a 3D feature-based visual tracker for tracking multiple moving objects by using a predictor that first partitions 3D features into different common-motion clusters and then predicts the motion of each cluster with Kalman filters. The 3D features are computed from a sequence of stereo images by combining two 2D temporal matching modules and one stereo correspondence module. To partition the 3D features into different common-motion clusters, we propose a RANSAC-based clustering method by using rigid body consensus which assumes that all the extracted 3D features on a rigid body have the same 3D motion. By using the motion estimates obtained with the RANSAC-based method as the measurements, we are able to use linear Kalman filters to predict the motion of each cluster, and then, to predict the next position of each 3D feature. Preliminary experiments showed that the proposed 3D predictive visual tracker can serve as a robust 3D feature tracker for an active stereo vision system.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
K. S. Arun, T. S. Huang, and S.D. Blostein, ”Least—Square Fitting of Two 3-D Point Sets,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 9, NO. 5, pp. 698–700, 1987.
T. K. Cheng and L. Kitchen, ”Preliminary Results on Real-Time 3D Feature-Based Tracker,” DICTA-93 Conf., Australian Pattern Recognition Society, Sydney, Dec. 1993.
M. A. Fischler and R. C. Bolles, ”Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, Jun. 1981, pp. 381–395.
Y. P. Hung, C. Y. Tang, S. W. Shih, Z. Chen and W. S. Lin, ”A 3D Feature-Based Tracker for Tracking Multiple Moving Objects with a Controlled Binocular Head,” Technique Report TR-IIS-95-004, Institute of Information Science, Academia Sinica, Taiwan, 1995.
K. Kanatani, Group-Theoretical Methods in Image Understanding. Springer-Verlag 1990.
D. Koller, K. Daniilidis and H. H. Nagel, ”Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes,” Int. J. Computer Vision, Vol. 10, No. 3, 1993, pp. 257–281.
M. S. Lew, T. S. Huang, and K. Wong, ”Learning and Feature Selection in Stereo Matching,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 16, No. 9, Sep. 1994, pp. 869–881.
S. W. Shih, Y. P. Hung and W. S. Lin. “Accurate Linear Techniques for Camera Calibration Considering Lens Distortion by Solving an Eigenvalue Problem,” Optical Engineering, vol. 32, No. 1, 1993, pp. 138–149.
S. W. Shih, Y. P. Hung and W. S. Lin, “Kinematic Parameter Identification of a Binocular Head Using Stereo Measurements of a Single Calibration Point,” Proceedings IEEE International Conference on Robotics and Automation, May 1995, pp. 1796–1801.
Z. Zhang and O. Faugeras, 3D Dynamic Scene Analysis: A Stereo Based Approach. Springer-Verlag 1992.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hung, YP., Tang, CY., Shih, SW., Chen, Z., Lin, WS. (1995). A 3D predictive visual tracker for tracking multiple moving objects with a stereo vision system. In: Chin, R.T., Ip, H.H.S., Naiman, A.C., Pong, TC. (eds) Image Analysis Applications and Computer Graphics. ICSC 1995. Lecture Notes in Computer Science, vol 1024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60697-1_83
Download citation
DOI: https://doi.org/10.1007/3-540-60697-1_83
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60697-0
Online ISBN: 978-3-540-49298-6
eBook Packages: Springer Book Archive