Evaluation of Local Features Using Convolutional Neural Networks for Person Re-Identification

摘要

In this paper, we mainly evaluate the influence of local features extracted by convolutional neural networks for person re-identification. Considering the variant body parts with different structural information, we divide the holistic person images into several parts and extract their features. Two kinds of aggregation methods are used to aggregate local features. Experiments on the challenging person re-identification database, Market-1501 database, show that the max aggregation is more effective for extracting the discriminative local features than the sum aggregation.