Self-selective receptive field network for person re-identification

Abstract 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.