Spatially constrained level-set tracking and segmentation of non-rigid objects |
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| Affiliation: | 1. State Key Lab of CAD&CG, Zhejiang University, China;2. Software School of Xiamen University, China;1. School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, PR China;2. Department of Electronic Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli City, Taoyuan County 320, Taiwan, ROC;1. Department of Biomedical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu 211106, China;2. Department of Radiology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou 510006, China;1. Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, PR China;2. Collaborative Innovation Center of Social Safety Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, PR China;3. The 28th Research Institute of China Electronic Technology Group Corporation, Nanjing 210007, PR China;4. State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, PR China;1. Department of Mathematics and Physics, North China Electric Power University, China;2. School of Science, Communication University of China, China;1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;3. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA |
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| Abstract: | Level-set is a widely used technique in segmentation-based tracking due to its flexibility in handling 2D topological changes and computational efficiency. Most existing level-set models aim at grouping pixels that have similar features into a region, without consideration of the spatial relationship of these pixels. In this paper, we present a novel level-set tracking method that incorporates spatial information to improve the robustness and accuracy of tracking non-rigid objects. Both tracking and segmentation are performed in a unified probabilistic framework, with additional spatial constraints from a part-based model—the Hough Forests. In the stage of tracking, the rigid motion of the target object is estimated by rigid registration in both the color space and the Hough voting space. Then in the stage of segmentation, some support points are obtained from back-projection, and guide the level-set evolution to capture the shape deformation. We conduct quantitative evaluation on two recently proposed public benchmarks: a non-rigid object tracking dataset and the CVPR2013 online tracking benchmark, involving 61 sequences in total. The experimental results demonstrate that our tracking method performs comparably to the state-of-the-arts in the CVPR2013 benchmark, while shows significantly improved performance in tracking non-rigid objects. |
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| Keywords: | Segmentation-based tracking Level-set Hough voting Back-projection |
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