Scale Voting with Pyramidal Feature Fusion Network for Person Search

Abstract

Person search aims to find the target person among a large gallery set of real scene images. Candidates should be detected and cropped before recognizing their identifications. The detection process results in a great variance of object image scales. To address this we propose Pyramidal Feature Fusion Network that integrates the top-down mid-level features to provide multi-level feature outputs. Furthermore, we propose to apply each of the mid-level features independently in ranking and we design a ranking algorithm named Scale Voting that votes among these ranking results from different feature levels to get the final ranking order. In this approach, we can make better use of the diversity and consistency information that is hidden in different mid-levels of features. The proposed algorithm achieves the state-of-the-art performance on prevalent person search datasets.