Lightweight Residual Network for Person Re-identification
Reza Fuad Rachmadi, Supeno Mardi Susiki Nugroho, I Ketut Eddy Purnama · IOP Conference Series Materials Science and Engineering · 2021
Abstract In this paper, we investigated several lightweight convolutional neural network (CNN) classifiers for person re-identification problems. Person re-identification can be described as finding a person by performing a query with a specific person image on some defined gallery of people images. We construct the lightweight CNN classifier by utilizing the lightweight residual network architectures originally used for the CIFAR dataset. The goal is to design a lightweight CNN classifier with a maximum of 3 million number of parameters that produce good accuracy on person re-identification problems. The last layer of the residual network is detached and changed by two fully-connected layers, one for learning the discriminant features, and the other fully-connected layer for calculated the final classification score in the training process. Experiment results show that the ensemble of lightweight residual networks achieved a good performance on Market-1501 (rank-1 accuracy of 89.46% with mAP of 84.48% for single-query and rank-1 accuracy of 92.23% with mAP of 88.07% for multi-query). Although the lightweight residual network does not achieve state-of-the-art performance, our analysis shows that the lightweight residual network has higher information density comparing with other state-of-the-art models. More information density means that the classifier is more efficient in terms of performance with the number of parameters in the classifier. Implementation of these experiments are available at https://github.com/rezafuad/ person-reid-lightweight-residualnet.