Abstract
Until now, most existing researches on person re-identification aim at improving the recognition rate on single dataset setting. The training data and testing data of these methods are form the same source. Although they have obtained high recognition rate in experiments, they usually perform poorly in practical applications. In this paper, we focus on the cross dataset person re-identification which make more sense in the real world. We present a deep learning framework based on convolutional neural networks to learn the person representation instead of existing hand-crafted features, and cosine metric is used to calculate the similarity. Three different datasets Shinpuhkan2014dataset, CUHK and CASPR are chosen as the training sets, we evaluate the performances of the learned person representations on VIPeR. For the training set Shinpuhkan2014dataset, we also evaluate the performances on PRID and iLIDS. Experiments show that our method outperforms the existing cross dataset methods significantly and even approaches the performances of some methods in single dataset setting.
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Acknowledgment
This work was supported by the Chinese National Natural Science Foundation Projects #61105023, #61103156, #61105037, #61203267, #61375037, National Science and Technology Support Program Project #2013BAK02B01, Chinese Academy of Sciences Project No. KGZD-EW-102-2, and AuthenMetric R&D Funds.
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Hu, Y., Yi, D., Liao, S., Lei, Z., Li, S.Z. (2015). Cross Dataset Person Re-identification. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_47
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DOI: https://doi.org/10.1007/978-3-319-16634-6_47
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