Robust Target Tracking by Online Random Forests and Superpixels
文献类型：期刊论文
| 作者 | Wang, Wei1,2; Wang, Chunping2; Liu, Si1; Zhang, Tianzhu3 ; Cao, Xiaochun1
|
| 刊名 | IEEE Transactions on Circuits and Systems for Video Technology
 |
| 出版日期 | 2017-03-20 |
| 期号 | 99页码:1 - 1 |
| 关键词 | Superpixels Vision Tracking Online Random Forests Joint Discriminative Model |
| 英文摘要 | This paper presents a robust Joint Discriminative appearance model based Tracking method using online random forests and mid-level feature (superpixels). To achieve superpixelwise discriminative ability, we propose a joint appearance model that consists of two random forest based models, i.e., the Background-Target discriminative Model (BTM) and Distractor- Target discriminative Model (DTM). More specifically, the BTM effectively learns discriminative information between the target object and background. In contrast, the DTM is used to suppress distracting superpixels which significantly improves the tracker’s robustness and alleviates the drifting problem. A novel online random forest regression algorithm is proposed to build the two models. The BTM and DTM are linearly combined into a joint model to compute a confidence map. Tracking results are estimated using the confidence map, where the position and scale of the target are estimated orderly. Furthermore, we design a model updating strategy to adapt the appearance changes over time by discarding degraded trees of the BTM and DTM and initializing new trees as replacements. We test the proposed tracking method on two large tracking benchmarks, the CVPR2013 tracking benchmark and VOT2014 tracking challenge. Experimental results show that the tracker runs at real-time speed and achieves favorable tracking performance compared with the state-of-the-art methods. The results also suggest that the DTM improves tracking performance significantly and plays an important role in robust tracking. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20462]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China 2.the 2nd Department, Ordnance Engineering College, Shijiazhuang, 050003, China 3.State Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100090, China |
| 推荐引用方式 GB/T 7714 | Wang, Wei,Wang, Chunping,Liu, Si,et al. Robust Target Tracking by Online Random Forests and Superpixels[J]. IEEE Transactions on Circuits and Systems for Video Technology,2017(99):1 - 1. |
| APA | Wang, Wei,Wang, Chunping,Liu, Si,Zhang, Tianzhu,&Cao, Xiaochun.(2017).Robust Target Tracking by Online Random Forests and Superpixels.IEEE Transactions on Circuits and Systems for Video Technology(99),1 - 1. |
| MLA | Wang, Wei,et al."Robust Target Tracking by Online Random Forests and Superpixels".IEEE Transactions on Circuits and Systems for Video Technology .99(2017):1 - 1. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。