What And Where To Focus In Person Search

Abstract

Person search aims to locate and identify the query person from a gallery of original scene images. Almost all previous methods only consider single high-level semantic information, ignoring that the essence of identification task is to learn rich and expressive features. Additionally, large pose variations and occlusions of the target person significantly increase the difficulty of search task. For these two findings, we first propose multilevel semantic aggregation algorithm for more discriminative feature descriptors. Then, a pose-assisted attention module is designed to highlight fine-grained area of the target and simultaneously capture valuable clues for identification. Extensive experiments confirm that our framework can coordinate multilevel semantics of persons and effectively alleviate the adverse effects of occlusion and various pose. We also achieve state-of-the-art performance on two challenging datasets CUHK-SYSU and PRW.