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A loss combination based deep model for person re-identification

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Abstract

The Convolutional Neural Network (CNN) has significantly improved the state-of-the-art in person re-identification (re-ID). In the existing available identification CNN model, the softmax loss function is employed as the supervision signal to train the CNN model. However, the softmax loss only encourages the separability of the learned deep features between different identities. The distinguishing intra-class variations have not been considered during the training process of CNN model. In order to minimize the intra-class variations and then improve the discriminative ability of CNN model, this paper combines a new supervision signal with original softmax loss for person re-ID. Specifically, during the training process, a center of deep features is learned for each pedestrian identity and the deep features are subtracted from the corresponding identity centers, simultaneously. So that, the deep features of the same identity to the center will be pulled efficiently. With the combination of loss functions, the inter-class dispersion and intra-class aggregation can be constrained as much as possible. In this way, a more discriminative CNN model, which has two key learning objectives, can be learned to extract deep features for person re-ID task. We evaluate our method in two identification CNN models (i.e., CaffeNet and ResNet-50). It is encouraging to see that our method has a stable improvement compared with the baseline and yields a competitive performance to the state-of-the-art person re-ID methods on three important person re-ID benchmarks (i.e., Market-1501, CUHK03 and MARS).

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Notes

  1. Here, we do not provide detailed descriptions of the identification CNN model and just take CaffeNet [25] model as instance in Fig. 2.

  2. The size is 227 ×227 for CaffeNet [25], while is 224 ×224 for ResNet-50 [19].

  3. Note: CaffeNet [25] is FC7 layer, while ResNet-50 [19] is Pool5 layer.

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Acknowledgements

This work is supported by the Foundation for Innovative Research Groups of the NSFC (Grant no.71421001), National Natural Science Foundation of China (Grant no.61502073), and the Open Projects Program of National Laboratory of Pattern Recognition (No.201407349).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Fuqing Zhu, Xiangwei Kong, Haiyan Fu & Ming Li

  2. General Design Institute, Zhejiang Sci-Tech University, Hangzhou, 310018, China

    Qun Wu

  3. Taizhou Research Institute, Zhejiang University, Taizhou, 318000, China

    Qun Wu

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  1. Fuqing Zhu
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Correspondence to Xiangwei Kong.

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Zhu, F., Kong, X., Wu, Q. et al. A loss combination based deep model for person re-identification. Multimed Tools Appl 77, 3049–3069 (2018). https://doi.org/10.1007/s11042-017-5009-y

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