Abstract. Person reidentification (ReID) is an important issue in the field of image processing and computer vision. Because pedestrian images are often affected by various interference factors, such as occlusion, illumination changes, posture changes, and background changes, extracting discriminative features is an important method to improve the accuracy of ReID. Based on the two existing methods of pose-sensitive embedding and batch feature erasing, a new feature extraction model for person ReID tasks is proposed. The model uses the view information as global features and uses the batch feature erasure method to extract fine-grained features. The mutual complementarity of the two features improves the accuracy of person ReID. In addition, by introducing the attention module, the structure of the complex network becomes concise and the amount of calculation becomes smaller. Through a large number of experiments on three public datasets, it can be seen that the proposed model can effectively deal with the occlusion environment, and it can also obtain competitive results when compared with other state-of-the-art models.

中文翻译：

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基于视图信息和批量特征擦除的人员重新识别

摘要。行人重识别（ReID）是图像处理和计算机视觉领域的一个重要问题。由于行人图像经常受到各种干扰因素的影响，如遮挡、光照变化、姿态变化、背景变化等，提取判别特征是提高ReID准确率的重要方法。基于现有的姿态敏感嵌入和批量特征擦除两种方法，提出了一种新的行人 ReID 任务特征提取模型。该模型使用视图信息作为全局特征，并使用批量特征擦除方法提取细粒度特征。这两个特征的互补性提高了行人 ReID 的准确性。此外，通过引入注意力模块，复杂网络的结构变得简洁，计算量变小。通过在三个公共数据集上的大量实验可以看出，所提出的模型可以有效地处理遮挡环境，并且与其他最先进的模型相比，它也可以获得具有竞争力的结果。