JOTS: Joint Online Tracking and Segmentation
JOTS: Joint Online Tracking and Segmentation
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DOI：
10.1109/cvpr.2015.7298835
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Longyin Wen;Dawei Du;Zhen Lei;S. Li;Ming-Hsuan Yang
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文献类型：
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作者：
Longyin Wen;Dawei Du;Zhen Lei;S. Li;Ming-Hsuan Yang
我们提出了一种新的联合在线跟踪和分割（JOTS）算法，它集成了多部分跟踪和分割到一个统一的能量优化框架来处理视频分割任务。多部分分割被视为一个像素级的标签分配任务与正则化根据估计的部分模型，和跟踪制定为估计的部分模型的基础上的像素标签，这反过来又被用来细化模型。多部分跟踪和分割迭代进行，以最小化建议的目标函数的RANSAC风格的方法。在SegTrack和SegTrack v2数据库上进行的大量实验表明，该算法与现有的方法相比具有良好的性能。
We present a novel Joint Online Tracking and Segmentation (JOTS) algorithm which integrates the multi-part tracking and segmentation into a unified energy optimization framework to handle the video segmentation task. The multi-part segmentation is posed as a pixel-level label assignment task with regularization according to the estimated part models, and tracking is formulated as estimating the part models based on the pixel labels, which in turn is used to refine the model. The multi-part tracking and segmentation are carried out iteratively to minimize the proposed objective function by a RANSAC-style approach. Extensive experiments on the SegTrack and SegTrack v2 databases demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.