Query-Guided End-To-End Person Search

Bharti Munjal, Sikandar Amin, Federico Tombari, Fabio Galasso; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 811-820

Abstract


Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii. the person search should leverage the query image extensively (e.g. emphasizing unique query patterns). However, so far, no prior art realizes this. We introduce a novel query-guided end-to-end person search network (QEEPS) to address both aspects. We leverage a most recent joint detector and re-identification work, OIM [37]. We extend this with i. a query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global context from both the query and gallery images, ii. a query-guided region proposal network (QRPN) to produce query-relevant proposals, and iii. a query-guided similarity subnetwork (QSimNet), to learn a query-guided re-identification score. QEEPS is the first end-to-end query-guided detection and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46] datasets, we outperform the previous state-of-the-art by a large margin.

Related Material


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[bibtex]
@InProceedings{Munjal_2019_CVPR,
author = {Munjal, Bharti and Amin, Sikandar and Tombari, Federico and Galasso, Fabio},
title = {Query-Guided End-To-End Person Search},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}