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.
This work was supported by Korean Ministry of Science and Technology under the National Research Lab. Project, by Korean Ministry of Education under Brain Korea 21 Project, by the University Research Program in Robotics under grant DOE-R01-1344148, by the DOD/TACOM/NAC/ARC Program R01-1344-18, and by FAA/NSSA Program, R01-1344-48/49.
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Kim, S. et al. (2004). Optical Flow-Based Tracking of Deformable Objects Using a Non-prior Training Active Feature Model. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_10
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DOI: https://doi.org/10.1007/978-3-540-30543-9_10
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