L2-RLS-Based Object Tracking
L2-RLS-Based Object Tracking
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DOI：
10.1109/tcsvt.2013.2291355
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发表时间：
2014-08
影响因子：
8.4
通讯作者：
Ziyang Xiao;Huchuan Lu;D. Wang
中科院分区：
文献类型：
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作者：
Ziyang Xiao;Huchuan Lu;D. Wang
在本文中，我们提出了一种鲁棒的快速跟踪算法，该算法通过在贝叶斯推理框架中求解2-正则化最小二乘问题来实现目标跟踪。首先，利用主成分分析基向量和平方模板对跟踪目标的外观变化进行建模，使跟踪器既利用了子空间表示的强度，又明确地考虑了局部遮挡；它们可以很好地表示完整和损坏的对象。其次，我们采用l2正则化最小二乘法对所提出的表示模型进行求解。与基于复数的算法相比，该算法在处理跟踪问题时，在不损失精度的情况下提供了非常快的性能。此外，新的似然函数和改进的更新方案进一步提高了跟踪器的鲁棒性。对几个具有挑战性的图像序列的定性和定量评估表明，该方法优于几种最先进的跟踪算法。
In this paper, we present a robust and fast tracking algorithm in which object tracking is achieved by solving ℓ2-regularized least square (ℓ2-RLS) problems in a Bayesian inference framework. First, the changing appearance of the tracked target is modeled with PCA basis vectors and square templates, which makes the tracker not only exploit the strength of subspace representation but also explicitly take partial occlusion into consideration. They can together represent both the intact and corrupted objects well. Second, we adopt the ℓ2-regularized least square method to solve the proposed representation model. Compared with the complex ℓ1-based algorithm, it provides a very fast performance without the loss of accuracy in handling the tracking problem. In addition, a novel likelihood function and a refined update scheme further help to improve the robustness of our tracker. Both qualitative and quantitative evaluations on several challenging image sequences demonstrate that the proposed method performs favorably against several state-of-the-art tracking algorithms.