CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Robust Target Tracking by Online Random Forests and Superpixels
Wang, Wei1,2; Wang, Chunping2; Liu, Si1; Zhang, Tianzhu3; Cao, Xiaochun1
Source PublicationIEEE Transactions on Circuits and Systems for Video Technology
2017-03-20
Issue99Pages:1 - 1
AbstractThis 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.
KeywordSuperpixels Vision Tracking Online Random Forests Joint Discriminative Model
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20462
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.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
Recommended Citation
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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