eprintid: 10115347
rev_number: 14
eprint_status: archive
userid: 608
dir: disk0/10/11/53/47
datestamp: 2020-11-18 13:22:30
lastmod: 2021-12-16 01:43:29
status_changed: 2020-11-18 13:22:30
type: proceedings_section
metadata_visibility: show
creators_name: Ponce-Lopez, V
creators_name: Burghardt, T
creators_name: Sun, Y
creators_name: Hannuna, S
creators_name: Damen, D
creators_name: Mirmehdi, M
title: Deep Compact Person Re-Identification with Distractor Synthesis via Guided DC-GANs
ispublished: pub
divisions: UCL
divisions: B04
divisions: C04
divisions: F34
keywords: Person Re-ID, GANs, Distractor synthesis, Deep face analysis
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: We present a dual-stream CNN that learns both appearance and facial features in tandem from still images and, after feature fusion, infers person identities. We then describe an alternative architecture of a single, lightweight ID-CondenseNet where a face detector-guided DC-GAN is used to generate distractor person images for enhanced training. For evaluation, we test both architectures on FLIMA, a new extension of an existing person re-identification dataset with added frame-by-frame annotations of face presence. Although the dual-stream CNN can outperform the CondenseNet approach on FLIMA, we show that the latter surpasses all state-of-the-art architectures in top-1 ranking performance when applied to the largest existing person re-identification dataset, MSMT17. We conclude that whilst re-identification performance is highly sensitive to the structure of datasets, distractor augmentation and network compression have a role to play for enhancing performance characteristics for larger scale applications.
date: 2019-09-02
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-030-30642-7_44
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1818993
doi: 10.1007/978-3-030-30642-7_44
isbn_13: 978-3-030-30641-0
medium: 10.1007/978-3-030-30642-7_44
lyricists_name: Ponce Lopez, Victor
lyricists_id: VPONC48
actors_name: Dewerpe, Marie
actors_id: MDDEW97
actors_role: owner
full_text_status: public
series: Lecture Notes in Computer Science
publication: IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT I
volume: 11751
place_of_pub: Cham, Switzerland
pagerange: 488-498
pages: 11
event_title: 20th International Conference on Image Analysis and Processing (ICIAP)
event_location: Univ Trento, Fac Law, Trento, ITALY
event_dates: 09 September 2019 - 13 September 2019
institution: 20th International Conference on Image Analysis and Processing (ICIAP)
issn: 1611-3349
book_title: Image Analysis and Processing – ICIAP 2019. ICIAP 2019
editors_name: Ricci, E
editors_name: Bulo, SR
editors_name: Snoek, C
editors_name: Lanz, O
editors_name: Messelodi, S
editors_name: Sebe, N
citation:        Ponce-Lopez, V;    Burghardt, T;    Sun, Y;    Hannuna, S;    Damen, D;    Mirmehdi, M;      (2019)    Deep Compact Person Re-Identification with Distractor Synthesis via Guided DC-GANs.                     In: Ricci, E and Bulo, SR and Snoek, C and Lanz, O and Messelodi, S and Sebe, N, (eds.) Image Analysis and Processing – ICIAP 2019. ICIAP 2019.  (pp. pp. 488-498).  Springer: Cham, Switzerland.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10115347/1/Ponce%20Lopez_ICIAP19_Guided-DC-GANs_CameraReady.pdf