A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views

Authors:
Soon Ki Jung;Kwang Yun Wohn
Affiliations:
-;-
Venue:
Pattern Recognition Letters
Year:
1998

Citing 8
Cited 3

Finding Trajectories of Feature Points in a Monocular Image Sequence

IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D motion estimation, understanding, and prediction from nosiy image sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic monocular machine vision

Machine Vision and Applications
3-D Motion Estimation Using a Sequence of Noisy Stereo Images: Models, Estimation, and Uniqueness Results

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking feature points in time-varying images using an opportunistic selection approach

Pattern Recognition
Robust model-based motion tracking through the integration of search and estimation

International Journal of Computer Vision
A review of statistical data association for motion correspondence

International Journal of Computer Vision
Improved Computational Methods for Ray Tracing

ACM Transactions on Graphics (TOG)

Rapid Object Tracking on Compressed Video

PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Correlation-based particle filter for 3D object tracking

Integrated Computer-Aided Engineering
Object tracking based on the combination of learning and cascade particle filter

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

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Abstract

A method of tracking multiple objects of known geometry using multiple cameras is proposed. Our approach differs from the previous approaches in that the object geometry is tightly integrated into the tracking process. The major contribution is threefold: Firstly, multiple cameras are used to improve the accuracy of the estimated posture parameters. Additional formalism required by considering multiple images is nicely integrated into the tracking model, and is handled effectively. Secondly, the feature tracking is facilitated by integrating the measurement and dynamic models into the matching process, thereby improving the accuracy and robustness of the feature correspondence. Thirdly, ambiguities that may arise in the course of the feature matching are resolved by the statistical analysis and the visibility test. The entire process from the image sequence to the posture parameters has been completely automated into a single, seamless process, and has been extensively tested on synthetic and real images.