Extended compressed tracking via random projection based on MSERs and online LS-SVM learning
Extended compressed tracking via random projection based on MSERs and online LS-SVM learning
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DOI：
10.1016/j.patcog.2016.02.012
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Yuefang Gao;Xin Shan;Zexi Hu;Dong Wang;Ya Li;Xuhong Tian
中科院分区：
文献类型：
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作者：
Yuefang Gao;Xin Shan;Zexi Hu;Dong Wang;Ya Li;Xuhong Tian
压缩跟踪算法（CT跟踪器）是一种著名的视觉跟踪方法，它通过稀疏随机投影来模拟目标对象的外观。然而，由于随机投影的随机性，跟踪结果不稳定和鲁棒性。针对这一问题，提出了一种基于最大稳定极值区域（MSERs）、稀疏随机投影和在线最小二乘支持向量机分类器（LS-SVM）学习的视觉跟踪方法。为了获得一个相对稳定的外观模型，在图像特征空间中的MSER的基础上提取的稳定连接组件的对象。将MSER与稀疏随机投影相融合，建立自适应的物体外观模型，以适应物体外观的变化。此外，一个在线的封闭形式的LS-SVM被用来快速和鲁棒地预测目标物体的位置在跟踪检测框架。基准序列上的实验结果表明，与现有的基于CT的跟踪器和其他国家的最先进的跟踪器相比，该算法的稳定性和鲁棒性。
The compressed tracking algorithm (CT tracker) is a well-known visual tracking method that models a target object׳s appearance through sparse random projection. However, the tracking results are not stable and robust due to the randomness of random projection. To solve this problem, a more stable and robust approach is proposed for visual tracking based on maximally stable extremal regions (MSERs), sparse random projection and online least squares SVM classifier (LS-SVM) learning. To obtain a relatively stable appearance model, the stable connected components of an object based on MSERs in image feature space are extracted. With the fusion of MSERs and sparse random projection, we model adaptive object appearance to adapt the variation of appearance. Additionally, an online closed-form LS-SVM is employed to quickly and robustly predict the target object location in a tracking by detection framework. Experimental results on benchmark sequences show the stability and robustness of the proposed algorithm compared with the existing CT-based trackers and other state-of-the-art trackers.