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FMT: fusing multi-task convolutional neural network for person search

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Abstract

Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61872005, in part by the Natural Science Research Project of Anhui universities of China under Grant KJ2019A0005, KJ2019A0032, and in part supported by open project of Anhui University KF2019A03.

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Authors and Affiliations

  1. School of Mathematical Sciences, Anhui University, Hefei, 230601, China

    Sulan Zhai & Shunqiang Liu

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Xiao Wang & Jin Tang

Authors
  1. Sulan Zhai
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  2. Shunqiang Liu
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  3. Xiao Wang
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  4. Jin Tang
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Correspondence to Sulan Zhai.

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Zhai, S., Liu, S., Wang, X. et al. FMT: fusing multi-task convolutional neural network for person search. Multimed Tools Appl 78, 31605–31616 (2019). https://doi.org/10.1007/s11042-019-07939-w

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  • DOI: https://doi.org/10.1007/s11042-019-07939-w

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