Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function

@article{Cheng2016PersonRB,
  title={Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function},
  author={De Cheng and Yihong Gong and Sanping Zhou and Jinjun Wang and Nanning Zheng},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={1335-1344}
}
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full-body and local body-parts features of the input persons. The CNN model is trained by… CONTINUE READING
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