Robust Target Tracking by Online Random Forests and Superpixels
Wang, Wei1,2; Wang, Chunping2; Liu, Si1; Zhang, Tianzhu3; Cao, Xiaochun1
2017-03-20
发表期刊IEEE Transactions on Circuits and Systems for Video Technology
期号99页码:1 - 1
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.
关键词Superpixels Vision Tracking Online Random Forests Joint Discriminative Model
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20462
专题模式识别国家重点实验室_多媒体计算与图形学
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.
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