Mr. Bing Chen Profile

Bing Chen

at Air Force Engg Univ

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Proceedings Article | 6 May 2019 Paper

Full batch loss for person re-identification

Bing Chen, Yufei Zha, Min Wu, Yunqiang Li, Zhiqiang Hou

Proceedings Volume 11069, 110690H (2019) https://doi.org/10.1117/12.2524336

KEYWORDS: Data modeling, Performance modeling, Statistical modeling, Databases, Mining, Cameras, Visualization, Convolutional neural networks, Network architectures, Lithium

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Distance metric learning between pairs of samples is the key issue for the person re-identification(re-ID). Recently, convolutional neural networks (CNNs) significantly is employed to improve performance by learning deep semantic features which requires a large amount of labeled data for train the deep model. For person re-ID task, the training data is scarce. To the end, how to use the limited data to achieve the optimal deep model is a challenging problem. We observe that sample's similarity information and identity information are complementary. Therefore, a full-batch loss function is proposed in this paper, which promotes the distance relationship of individual sample pairs in a batch to the distance matrix in all samples to make full use of the sample similarity information. At the same time, it also makes full use of the sample's identity information by integrating the identification loss function. We conduct experiments on the two public person re-ID benchmarks: Market1501 and DukeMTMC-reID. The results clearly demonstrate that our proposed full batch loss model produces more discriminative descriptors for person re-ID, which outperforms well established baselines significantly and offer new state-of-the-art performances

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