Superpixel Random Erasing for Long-Term Person Re-identification

doi:10.3778/j.issn.1002-8331.2203-0023

Abstract

Abstract: Existing person re-identification often relies on the assumption that a pedestrian will not change his clothing. Unfortunately, this assumption may not be applicable to long-term person re-identification. In datasets captured over a long period of time, the clothing of pedestrians changes frequently. The current mainstream person re-identification methods often fail in long-term person re-identification, and their recognition accuracy will drop significantly. Aiming at the situation of clothing changes in long-term personre-identification, a superpixel random erasing algorithm is proposed. The algorithm assigns random values to superpixel blocks that may be clothing regions for erasing. Moreover, the images before and after erasing will be input to the backbone for training. In addition, the model output features before and after erasing are also constrained with a deep mean squared error loss, which forces the model to learn cloth-irrelevant features. Experiments show that the proposed method can effectively cope with the problem of pedestrian clothing change in long-time person re-identification, and the recognition accuracy is greatly improved compared with previous methods.

Key words: person re-identification, superpixel, random erasing, cloth-changing, long-term person re-identification

摘要： 现有的行人重识别往往依赖于一个假设，即行人不会更换他的服装。不幸的是，这种假设可能不适用于长时行人重识别。在长时间捕获的行人数据集中，行人的服装经常发生变化。目前主流的行人重识别方法在长时行人重识别中往往会失效，其识别精度会大幅度下降。针对长时行人重识别中的服装变化的情况，提出了一种超像素随机擦除算法。该算法首先选取可能为服装区域的超像素，再用随机值擦除其中像素，并且擦除前后的图像都会被输入至骨干网络进行训练。还对擦除前后模型输出特征用深度均方差损失进行约束，从而强制模型学习与服装无关的特征。实验表明，该方法能够有效地应对长时行人重识别中行人换装问题，与之前的方法相比，识别精度有较大提升。

关键词: 行人重识别, 超像素, 随机擦除, 换装, 长时行人重识别

LI Guodong, GUO Lijun. Superpixel Random Erasing for Long-Term Person Re-identification[J]. Computer Engineering and Applications, 2023, 59(10): 221-226.

李国栋, 郭立君. 基于超像素随机擦除的长时行人重识别[J]. 计算机工程与应用, 2023, 59(10): 221-226.

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