FMT:Fusing Multi-task Convolutional Neural Network for Person Search

• URL: http://arxiv.org/abs/2003.00406v1
• Date: Sun, 1 Mar 2020 05:20:47 GMT
• Title: FMT:Fusing Multi-task Convolutional Neural Network for Person Search
• Authors: Sulan Zhai, Shunqiang Liu, Xiao Wang, Jin Tang
• Abstract summary: We propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches.
• Score: 33.91664470686695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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