Person Re-identification (Re-ID) technology aims to solve the matching problem of the same pedestrians at different times and places, which has important application value in the field of public safety. At present, most scholars focus on designing complex models to improve the accuracy of Re-ID, but the high complexity of the model further restricts the practical application of Re-ID algorithm. To solve the above problems, this paper designs a lightweight Self-selective Receptive Field (SRF) block instead of directly designing complex models. Specifically, the module can be plug-and-play on the general backbone network, so as to significantly improve the performance of Re-ID while effectively controlling the amount of its own parameter and calculation: (1) the SRF block encodes pedestrian targets and image contexts at different scales by constructing pyramidal convolution group and allows the module to independently select the size of the receptive field through training by means of self-adaptive weighting; (2) in order to reduce the complexity of SRF block, we introduce a "channel scaling factor" and design a "grouped convolution operation" by constraining the channels of the feature map and changing the structure of the convolution kernel respectively. Experiments on multiple datasets show that SRF Network (SRFNet) for Re-ID can achieve a good balance between performance and complexity, which fully demonstrates the effectiveness of SRF block.

行人重识别（Re-ID）技术旨在解决同一行人在不同时间、地点的匹配问题，在公共安全领域具有重要的应用价值。目前大多数学者都致力于设计复杂的模型来提高Re-ID的准确率，但模型的高复杂度进一步制约了Re-ID算法的实际应用。为了解决上述问题，本文设计了一种轻量级的自选择感受野（SRF）模块，而不是直接设计复杂的模型。具体来说，该模块可以在通用骨干网络上即插即用，从而在有效控制自身参数量和计算量的同时，显着提高Re-ID的性能：（1）SRF块对行人目标进行编码，通过构建金字塔卷积组来获取不同尺度的图像上下文，并允许模块通过自适应权重的训练来独立选择感受野的大小； （2）为了降低SRF块的复杂度，我们引入了“通道缩放因子”，并分别通过约束特征图的通道和改变卷积核的结构来设计“分组卷积操作”。在多个数据集上的实验表明，用于Re-ID的SRF网络（SRFNet）可以在性能和复杂度之间取得良好的平衡，这充分证明了SRF块的有效性。