Full batch loss for person re-identification

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

doi:10.1117/12.2524336

6 May 2019 Full batch loss for person re-identification

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

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690H (2019) https://doi.org/10.1117/12.2524336
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

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

Citation Download Citation

Bing Chen, Yufei Zha, Min Wu, Yunqiang Li, and Zhiqiang Hou "Full batch loss for person re-identification", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690H (6 May 2019); https://doi.org/10.1117/12.2524336

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KEYWORDS

Data modeling

Performance modeling

Statistical modeling

Databases

Mining

Cameras

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