Cross-dataset person re-identification method based on multi-pool fusion and background elimination network |
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Authors: | Yanfeng LI Bin ZHANG Jia SUN Houjin CHEN Jinlei ZHU |
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Affiliation: | School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100093,China |
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Abstract: | The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset mAP for Market-1501 was 35.53%,which was 9.24% higher than ResNet50.Using MSMT17 as the training set,the cross-dataset mAP for DukeMTMC-reID was 41.45%,which was 10.72% higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance. |
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Keywords: | person re-identification cross-dataset background elimination multi-pool fusion deep learning |
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