Re-ID Driven Localization Refinement for Person Search

Chuchu Han, Jiacheng Ye, Yunshan Zhong, Xin Tan, Chi Zhang, Changxin Gao, Nong Sang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 9814-9823

Abstract


Person search aims at localizing and identifying a query person from a gallery of uncropped scene images. Different from person re-identification (re-ID), its performance also depends on the localization accuracy of a pedestrian detector. The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task. To alleviate this issue, we propose a re-ID driven localization refinement framework for providing the refined detection boxes for person search. Specifically, we develop a differentiable ROI transform layer to effectively transform the bounding boxes from the original images. Thus, the box coordinates can be supervised by the re-ID training other than the original detection task. With this supervision, the detector can generate more reliable bounding boxes, and the downstream re-ID model can produce more discriminative embeddings based on the refined person localizations. Extensive experimental results on the widely used benchmarks demonstrate that our proposed method performs favorably against the state-of-the-art person search methods.

Related Material


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[bibtex]
@InProceedings{Han_2019_ICCV,
author = {Han, Chuchu and Ye, Jiacheng and Zhong, Yunshan and Tan, Xin and Zhang, Chi and Gao, Changxin and Sang, Nong},
title = {Re-ID Driven Localization Refinement for Person Search},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}