Joint Person Objectness and Repulsion for Person Search

TL;DR

This paper introduces a novel OR similarity measure for person search that jointly considers objectness and repulsion to improve ranking accuracy and reduce distractor influence, leading to state-of-the-art results.

Contribution

It proposes a new OR similarity combining objectness and repulsion terms, enhancing person search performance over traditional methods.

Findings

Improves mAP from 92.32% to 93.23% on CUHK-SYSU

Increases mAP from 50.91% to 52.30% on PRW

Effectively reduces distractor similarity in person search

Abstract

Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its \emph{neighbors}, the gallery images having a higher similarity with the \emph{neighbors of probe} should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.