稳健单模型估计的清晰加权支持向量回归:应用于图像序列中的目标跟踪

F. Dufrenois, J. Colliez, D. Hamad
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引用次数: 4

摘要

支持向量回归(SVR)是目前公认的一种估计实值函数的方法。然而,对于计算机视觉应用中常见的训练数据集中的异常点和结构化异常点,标准的支持向量回归算法并不有效。在本文中,我们提出了一个加权版本的支持向量机的回归。提出的方法引入了一个自适应二值函数,允许从退化的训练数据集中提取主导模型。这个二元函数通过一一分解逐步将内线与离群分离。实验测试表明,该方法对异常值和残差结构化异常值具有较高的鲁棒性。接下来,我们验证了我们的算法用于目标跟踪和光流估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crisp Weighted Support Vector Regression for robust single model estimation : application to object tracking in image sequences
Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encountered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary function that allows a dominant model from a degraded training dataset to be extracted. This binary function progressively separates inliers from outliers following a one-against-all decomposition. Experimental tests show the high robustness of the proposed approach against outliers and residual structured outliers. Next, we validate our algorithm for object tracking and for optic flow estimation.
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