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3D object tracking via boundary constrained region-based model
Song Zhao; LingFeng Wang; Wei Sui; Huai-Yu Wu; Chunhong Pan
2014
Conference NameICIP2014
Source PublicationInternational Conference on Image Processing (ICIP)
Pages486-490
Conference Date2014
Conference PlaceParis, France
AbstractIn this paper, we propose a method for joint 2D segmentation and 2D-3D pose tracking. First, we define a novel energy functional which considers the discrimination between statistical appearance models and the coherence among neighboring pixels simultaneously. And then, a particle filter-like stochastic optimization technique is adopted to solve the energy functional, so that a preferable initial value can be provided for the subsequent damped Newton optimization method. Furthermore, an occlusion-aware updating strategy is utilized for appearance models, which can easily increase the foreground learning rate. As a result, our method is more suitable for the video sequences with occlusion. Experimental results highlight excellent performance on challenging synthetic and real-world sequences as compared with the state-of-the-art approaches.
KeywordBoundary Term Occlusion-aware Updating Pose Tracking Stochastic Optimization
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4735
Collection模式识别国家重点实验室_先进数据分析与学习
Recommended Citation
GB/T 7714
Song Zhao,LingFeng Wang,Wei Sui,et al. 3D object tracking via boundary constrained region-based model[C],2014:486-490.
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