Semantically Selective Augmentation for Deep Compact Person Re-Identification

Víctor Ponce-López*, Tilo Burghardt, Sion Hannunna, Dima Damen, Alessandro Masullo, Majid Mirmehdi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
79 Downloads (Pure)


We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
EditorsStefan Roth, Laura Leal-Taixé
PublisherSpringer Verlag
Number of pages11
ISBN (Electronic)978-3-030-11012-3
ISBN (Print)9783030110116
Publication statusPublished - 29 Jan 2019
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 8 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11130 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th European Conference on Computer Vision, ECCV 2018

Structured keywords

  • Digital Health


  • Adversarial synthesis
  • Deep compression
  • Face filtering
  • Person re-identification
  • Selective augmentation


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