Abstract
In public safety and social life, the task of Clothes-Changing Person Re-Identification (CC-ReID) has become increasingly significant. However, this task faces considerable challenges due to appearance changes caused by clothing alterations. Addressing this issue, this paper proposes an innovative method for disentangled feature extraction, effectively extracting discriminative features from pedestrian images that are invariant to clothing. This method leverages pedestrian parsing techniques to identify and retain features closely associated with individual identity while disregarding the variable nature of clothing attributes. Furthermore, this study introduces a gated channel attention mechanism, which, by adjusting the network's focus, aids the model in more effectively learning and emphasizing features critical for pedestrian identity recognition. Extensive experiments conducted on two standard CC-ReID datasets validate the effectiveness of the proposed approach, with performance surpassing current leading solutions. The Top-1 accuracy under clothing change scenarios on the PRCC and VC-Clothes datasets reached 64.8% and 83.7%, respectively.
Abstract (translated)
在公共安全和社会生活中，衣服变换行人重识别（CC-ReID）任务变得越来越重要。然而，由于服装变化导致的外观变化，这一任务面临着相当大的挑战。为了解决这个问题，本文提出了一种创新的分离特征提取方法，能够有效地从行人的图像中提取出不受服装影响的判别性特征。该方法利用行人解析技术来识别和保留与个体身份密切相关的特征，同时忽略服装属性的变化。此外，本研究引入了门控通道注意力机制，通过调整网络的关注点，帮助模型更有效地学习并强调对于行人身份识别至关重要的特征。在两个标准CC-ReID数据集上进行的广泛实验验证了所提出方法的有效性，其性能超越了当前领先的解决方案。在PRCC和VC-Clothes数据集上的衣服变化场景下，Top-1准确率分别达到了64.8%和83.7%。
URL
https://arxiv.org/abs/2410.21663
