Semantically Selective Augmentation for Deep Compact Person Re-Identification

  • Víctor Ponce-LópezEmail author
  • Tilo Burghardt
  • Sion Hannunna
  • Dima Damen
  • Alessandro Masullo
  • Majid Mirmehdi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


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.


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



This work was performed under the SPHERE IRC funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/K031910/1.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of EngineeringUniversity of BristolBristolUK

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