| Online structured hough forests for visual tracking | |
| Qin, Tao ; Zhong, Bin ; Wang, Hanzi ; Wang HZ(王菡子) | |
| 2013-10-18 | |
| 关键词 | Forestry Image segmentation Signal processing Tracking (position) |
| 英文摘要 | Conference Name:2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013. Conference Address: Vancouver, BC, Canada. Time:May 26, 2013 - May 31, 2013.; IEE Signal Processing Society; Segmentation-based tracking methods are popular in alleviating the model drift problem during online-learning of visual trackers. However, one of the limitations of those methods is that tracking results guide the process of segmentation. The model drift problem in tracking may have significant influence on segmentation. In this paper, we propose an online structured Hough Forests to address this limitation. The results of object tracking do not have significant influence on the process of segmentation. Our algorithm shows more robust results on several challenging sequences. ? 2013 IEEE. |
| 语种 | 英语 |
| 出处 | http://dx.doi.org/10.1109/ICASSP.2013.6638070 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 内容类型 | 其他 |
| 源URL | [http://dspace.xmu.edu.cn/handle/2288/86681]  |
| 专题 | 信息技术－会议论文 |
| 推荐引用方式 GB/T 7714 | Qin, Tao,Zhong, Bin,Wang, Hanzi,et al. Online structured hough forests for visual tracking. 2013-10-18. |
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