An Improved Convolutional Network Architecture Based on Residual Modeling for Person Re-Identification in Edge Computing

Person re-identification is an important task in the field of video surveillance that concentrates on identifying the same person across different cameras. Some methods cannot learn effective image representations, due to the low resolution of pedestrian image data sets. In this article, we propose a novel Siamese network architecture with layers specially designed to address the problem of re-identification. The architecture proposed in this work is applied to the edge of the cloud infrastructure, which can accelerate the speed of pedestrian retrieval. Our network outputs a similarity value when a pair of images is given as input, indicating whether the two input images show the same person. Novel elements of our architecture include a residual model layer that includes an "identity block'' and a "conv'' block, which considerably capture more efficient features between the two input images. A global average pooling layer is adopted to reduce the model complexity before a fully connected layer, which minimizes person retrieval time in edge computing. Our proposed method significantly improves previous on: CUHK03 by 30% in rank-1, Market-1501 by 35% in rank-1. We also demonstrate that the proposed method outperforms most state-of-the-art methods on the two public benchmarks.