Person Re-Identification by Support Vector Ranking
Person Re-Identification by Support Vector Ranking
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DOI：
10.5244/c.24.21
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发表时间：
2010
影响因子：
4.9
通讯作者：
B. Prosser;Weishi Zheng;S. Gong;T. Xiang
中科院分区：
文献类型：
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作者：
B. Prosser;Weishi Zheng;S. Gong;T. Xiang
解决人的重新识别问题涉及到在不相交的相机视图中匹配个体的观察。这个问题在忙碌的公共场景中变得特别困难，因为可能匹配的数量非常高。由于不同的照明条件，视角和身体姿势在相机视图中造成的显着外观变化进一步加剧了这一点。为了解决这个问题，现有的方法集中在提取或学习判别特征，然后使用距离度量进行模板匹配。这项工作的新奇在于，我们将人员重新识别问题重新表述为排名问题，并学习一个子空间，其中潜在的真实匹配被赋予最高排名，而不是任何直接的距离度量。通过这样做，我们将人的重新识别问题从一个绝对的评分问题转化为一个相对的排名问题。我们进一步开发了一种新的Ensemble RankSVM，以克服现有的基于SVM的排名方法所面临的可扩展性限制问题。这种新模型大大减少了内存使用，因此更具可扩展性，同时保持高水平的性能。我们提出了广泛的实验来证明现有的模板匹配和分类模型的性能增益的建议的排名方法。
Solving the person re-identification problem involves matching observation s of individuals across disjoint camera views. The problem becomes particularly hard in a busy public scene as the number of possible matches is very high. This is further compounded by significant appearance changes due to varying lighting conditions, vie wing angles and body poses across camera views. To address this problem, existing approaches focus on extracting or learning discriminative features followed by template matching using a distance measure. The novelty of this work is that we reformulate the person reidentification problem as a ranking problem and learn a subspace where th e potential true match is given highest ranking rather than any direct distance measure. By doing so, we convert the person re-identification problem from an absolute scoring p roblem to a relative ranking problem. We further develop an novel Ensemble RankSVMto overcome the scalability limitation problem suffered by existing SVM-based ranking methods. This new model reduces significantly memory usage therefore is much more scalable, whilst maintaining high-level performance. We present extensive experiments to demonstrate the performance gain of the proposed ranking approach over existing template matching and classification models.