Optical flow-based tracking of deformable objects using a non-prior training active feature model

Authors:
Sangjin Kim;Jinyoung Kang;Jeongho Shin;Seongwon Lee;Joonki Paik;Sangkyu Kang;Besma Abidi;Mongi Abidi
Affiliations:
Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, TN
Venue:
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
Year:
2004

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Abstract

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.