Person re-identification based on improved attention mechanism and global pooling method

Deep network has become a new favorite for person re-identification (Re-ID), whose research focus is how to effectively extract the discriminative feature representation for pedestrians. In the paper, we propose a novel Re -ID network named as improved ReIDNet (iReIDNet), which can effectively extract the local and global multi -granular feature representations of pedestrians by a well-designed spatial feature transform and coordinate attention (SFTCA) mechanism together with improved global pooling (IGP) method. SFTCA utilizes channel adaptability and spatial location to infer a 2D attention map and can help iReIDNet to focus on the salient in-formation contained in pedestrian images. IGP makes iReIDNet capture more effectively the global information of the whole human body. Besides, to boost the recognition accuracy, we develop a weighted joint loss to guide the training of iReIDNet. Comprehensive experiments demonstrate the availability and superiority of iReIDNet over other Re-ID methods. The code is available at https://github.com/XuRuyu66/ iReIDNet.