CBRA: Color-based ranking aggregation for person re-identification

Raphael C. Prates, W. R. Schwartz
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引用次数: 28

Abstract

The problem of automatically tracking a pedestrian within camera networks with non-overlapping field-of-view, known as person re-identification, is a challenging task with still suboptimal results. Different features have been proposed in the literature, specially colors which achieved the best results when fused in a unique feature representation. Despite being better than considering individually, the fusion still does not explores all the feature discriminative power. Therefore, we propose the use of rank aggregation to improve the results. In this paper, we address the person re-identification problem using a Color-based Ranking Aggregation (CBRA) method, which explores different feature representations to obtain complementary ranking lists and combine them using the Stuart ranking aggregation method. The obtained experimental results demonstrate a great improvement in state-of-the-art, reaching top-1 rank recognition rates of 50.0% and 56.9% in the ViPER and PRTD450S data sets, respectively.
CBRA:用于人员重新识别的基于颜色的排名聚合
在具有非重叠视场的摄像机网络中自动跟踪行人的问题,即人的再识别,是一项具有挑战性的任务,其结果仍然不理想。文献中提出了不同的特征,特别是颜色,当融合在一个独特的特征表示中时,效果最好。尽管比单独考虑要好,但融合仍然没有探索到所有的特征判别能力。因此,我们建议使用秩聚合来改进结果。在本文中,我们使用基于颜色的排名聚合(CBRA)方法来解决人的再识别问题,该方法探索不同的特征表示来获得互补的排名列表,并使用Stuart排名聚合方法将它们组合在一起。实验结果表明,该方法在最先进水平上有了很大的提高,在ViPER和PRTD450S数据集上分别达到了50.0%和56.9%的top-1等级识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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