Visual tracking and learning using speeded up robust features |
| |
| Authors: | Jingyu Li Yulei Wang Yanjie Wang |
| |
| Affiliation: | 1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;2. Graduate University of Chinese Academy of Sciences, Beijing 100039, China;3. Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Duisburg 47057, Germany;1. Visual Computing Department, Institute for Infocomm Research, Singapore;2. Electrical and Computer Engineering, National University of Singapore, Singapore;1. Department of Electronics Enigneering, Madras Institute of Technology, Anna University, Chennai-44;2. Department of Information Technology, Madras Institute of Technology, Anna University, Chennai-44 |
| |
| Abstract: | A speeded up robust features (SURF) based optical flow algorithm is presented for visual tracking in real scenarios. SURF construct invariant features to correspond the blobs of interest across frames. Meanwhile, new feature-based optical flow algorithm is used to compute the warp matrix of a region centered on SURF key points. Furthermore, on-line visual learning for long-term tracking is performed using incremental object subspace method, which includes the correct update of the sample mean and appearance model. The proposed SURF based tracking and learning method contributes measurably to improving overall tracking performance. Experimental work demonstrates that the proposed strategy improves the performance of the classical optical flow algorithms in complicated real scenarios. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
