Chapter

Bio-inspired Computing – Theories and Applications

Volume 682 of the series Communications in Computer and Information Science pp 343-354

Date:

A Simple Deep Feature Representation for Person Re-identification

  • Shengke WangAffiliated withDepartment of Computer Science and Technology, Ocean University of China
  • , Lianghua DuanAffiliated withDepartment of Computer Science and Technology, Ocean University of China
  • , Yong ZhaoAffiliated withDepartment of Computer Science and Technology, Ocean University of China
  • , Junyu DongAffiliated withDepartment of Computer Science and Technology, Ocean University of China Email author 

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Abstract

Person re-identification (Re-ID) aims to match persons across non-overlapping camera views at different time. Typical person Re-ID models include two critical components: feature representation and metric learning. Due to the large variations in a persons appearance by different poses, viewpoints, illumination and occlusions, metric learning is always a necessary part in person Re-ID. In this paper, we propose a Deep person Feature Representation (DFR) learning frame-work based on a classification-oriented convolution neural network, and the DFR is directly used to calculate cosine distance for the similarity measure while with-out explicit metric learning. In the framework, Batch Normalization (BN) is applied before the ReLU layer to accelerate the convergence process, and with dropout strategy the DFR is only 64-dimension which makes the feature representation more effective and less noisy. Experiments demonstrate that our approach achieves the state-of-the-art results on most of the challenging datasets, especially on dataset of the largest scale CUHK03.

Keywords

Person re-identification Deep learning Distance measure Metric learning