Person Re-Identification via Recurrent Feature Aggregation

@inproceedings{Yan2016PersonRV,
  title={Person Re-Identification via Recurrent Feature Aggregation},
  author={Yichao Yan and Bingbing Ni and Zhichao Song and Chao Ma and Yan Yan and Xiaokang Yang},
  booktitle={ECCV},
  year={2016}
}
We address the person re-identification problem by exploiting a globally feature representation from a sequence of tracked human regions/patches; We show that a progressive fusion framework based on LSTM aggregates the frame-wise human region representation and yields a sequence level feature representation; Experimental results on two person re-identification benchmarks demonstrate that the proposed method performs favorably against state-of-the-art person re-identification methods. 
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A survey of approaches and trends in person re - identification

  • A. Bedagkar-Gala, S. K. Shah
  • Image Vision Computing
  • 2014

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