Exploiting matching local information for person re-identification

H. Nguyen, Hong-Quan Nguyen, Thuy-Binh Nguyen, Van-Chien Pham, Thi-Lan Le
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Abstract

Person re-identification task with the main aim is to associate the instances of the same person captured by different cameras in a surveillance camera network usually employs the detection results. As a consequence, misalignment of detected bounding boxes and background information are the two main factors that lead to reducing the performance of person re-identification.To tackle with these challenges, the state-of-art in person re-identification methods proposed to employ attention mechanism or body parts detection. However, these methods have high complexity and computational cost, which can be reduced by using Earth Movers Distance (EMD) instead. Therefore, this paper formulates local matching as a distance calculation of two probability distributions and applies Earth Movers Distance (EMD) to compute the optimal matching between two sets of stripes in order to address an issue in the AlignedReID++ method. Different experiments have been conducted on both single-shot and multi-shot person re-identification. The obtained results have shown the improved performance of the proposed method compared with the baseline method. The matching rates at rank1 obtained by the proposed method are 49.59%, 83.36%, and 78.47% on VIPeR, Marketl501-Partial, and DukeMTMCReID-Partial, respectively.
利用匹配的局部信息进行人员再识别
人再识别任务的主要目的是将不同摄像机捕获的同一人的实例关联起来,在监控摄像机网络中通常采用检测结果。因此,检测到的边界框和背景信息的不对齐是导致降低人员再识别性能的两个主要因素。为了解决这些问题,现有的人体再识别方法主要采用注意机制或身体部位检测。然而,这些方法具有较高的计算复杂度和计算成本,可以通过使用动土距离(EMD)来降低计算成本。因此,本文将局部匹配表述为两种概率分布的距离计算,并应用EMD (Earth Movers distance)计算两组条纹之间的最优匹配,以解决alignedreid++方法中的一个问题。分别对单发和多发两种情况下的人的再识别进行了不同的实验。结果表明,与基线方法相比,该方法的性能有所提高。该方法在VIPeR、Marketl501-Partial和DukeMTMCReID-Partial上获得的rank1匹配率分别为49.59%、83.36%和78.47%。
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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