Self-Regulation Feature Network for Person Reidentification

Abstract

The performance of person reidentification (ReID) has been significantly improved with the development of convolutional neural networks in the past few years. Most existing ReID methods focus on modifying the backbone network architecture to learn feature representations of persons; however, there are only a few methods that explore the potential performance of the network architecture to relearn features. In this article, we propose a self-regulation feature (SRF) scheme based on the network model, called SRFnet, to address the problem of the limited ability of the model for learning person features. The proposed SRFnet utilizes global branches to supervise local branches and deeply mine potential features of the network from the perspective of optimization to obtain more distinguished feature representations of persons. Different from methods that add an attention mechanism, our SRFnet can self-adjust feature learning by only adding loss functions between the local and global features of the network, and it does not require any additional convolution parameters. Extensive experiments on three public datasets show that the proposed SRFnet method achieves the results of mean average precision (mAP)/Rank-1 as 93.65%/95.28% on Market1501, 89.65%/91.79% on DuckMTMC-reID, 83.79%/82.00% on CUHK03 (labeled), and 81.92%/80.21% on CUHK03 (detected) datasets, respectively. Furthermore, experimental results on cross-domain datasets also demonstrate the effectiveness of the proposed SRFnet.