融合随机擦除和残差注意力网络的行人重识别

doi:10.3778/j.issn.1002-8331.2009-0082

摘要/Abstract

摘要： 传统的行人重识别方法依赖人工构造视觉特征，容易受到其他外界因素的影响，识别精度低。深度学习模型能自主地提取特征，但随着网络层数的加深会出现梯度消失情况，残差网络能缓解梯度消失问题，但提取出的特征信息未被合理使用。行人部分图像被遮挡是影响行人重识别准确性的另一个重要因素。针对上述问题提出了融合随机擦除和残差注意力网络的行人重识别算法。该算法：（1）在残差网络的基础上，引入注意力机制模块，通过强化有用的特征和抑制作用不大的特征来提升网络的判别能力。（2）引入随机擦除的数据增强方法，以便降低过拟合现象，同时提高网络泛化能力，解决行人重识别中遮挡问题。（3）使用triplet loss对融合网络进行监督训练，实现样本在特征空间中达到更好的聚类效果，提升行人重识别的准确率。实验表明，该算法在Market-1501数据集和DukeMTMC-reID数据集上能获取较高的识别精度。

关键词: 行人重识别, 随机擦除, 残差网络, 注意力机制, 深度学习

Abstract: Traditional pedestrian re-recognition methods rely on artificially constructed visual features, which are easily affected by other external factors and have low recognition accuracy. The deep learning model can extract features autonomously, but as the number of network layers deepens, the gradient disappears. The residual network can alleviate the gradient disappearance problem, but the extracted feature information is not used rationally. Partial occlusion of pedestrian images is another important factor affecting the accuracy of pedestrian re-identification. To solve the above problems, this paper proposes a pedestrian re-recognition algorithm combining random erasing and residual attention network. The algorithm：first, on the basis of the residual network, the attention mechanism module is introduced, and the discriminative ability of the network is improved by strengthening the useful features and the features with little inhibition. Second, introduce random erasing data enhancement method in order to reduce the over-fitting phenomenon, at the same time improve the network generalization ability, and solve the occlusion problem in pedestrian re-identification. Third, using triplet loss to supervise and train the fusion network to achieve better clustering effect of samples in the feature space and improve the accuracy of pedestrian re-recognition. Experiments show that the algorithm can obtain higher recognition accuracy on the Market-1501 dataset and DukeMTMC-reID dataset.

Key words: pedestrian re-identification, random erasing, residual network, attention mechanism, deep learning

厍向阳, 李蕊心, 叶鸥. 融合随机擦除和残差注意力网络的行人重识别[J]. 计算机工程与应用, 2022, 58(3): 215-221.

SHE Xiangyang, LI Ruixin, YE Ou. Pedestrian Re-identification Combining Random Erasing and Residual Attention Network[J]. Computer Engineering and Applications, 2022, 58(3): 215-221.

参考文献

[1] 宋婉茹，赵晴晴，陈昌红，等.行人重识别研究综述[J].智能系统学报，2017，12（6）：770-780.
SONG W R，ZHAO Q Q，CHEN C H，et al.Survey on pedestrian re-identification research[J].CAAI Transactions on Intelligent Systems，2017，12（6）：770-780.
[2] LIAO S，HU Y，ZHU X，et al.Person re-identification by local maximal occurrence representation and metric learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2015：2197-2206.
[3] MA B，YU S，JURIE F.Local descriptors encoded by fisher vectors for person re-identification[C]//International Conference on Computer Vision，2012：413-422.
[4] LIU H，MA B，QIN L，et al.Set-label modeling and deep metric learning on person re-identification[J].Neurocomputing，2015，151：1283-1292.
[5] WEINBERGERK Q，SAUL K L.Distance metric learning for large margin nearest neighbor classification[J].Journal of Machine Learning Research，2009，10（1）：207-244.
[6] GUILLAUMIN M，VERBEE J，SCHMID C.Is that you? Metric learning approaches for face identification[C]//Proceedings of the 12th International Conference on Computer Vision，Kyoto，Japan，2009：498-505.
[7] KOESTINGER M，HIRZER M，WOHLHART P，et al.Large scale metric learning from equivalence constraints[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition，2012：2288-2295.
[8] WANG G，YUAN Y，CHEN X，et al.Learning discriminative features with multiple granularities for person re?identification[C]//2018 ACM Conference on Multimedia，2018：274-282.
[9] LIN Y T，ZHENG L，ZHENG Z D，et al.Improving person re-identification by attribute andidentity learning[J].Pattern Recognition，2019，95：151-161.
[10] 徐家臻，李婷，杨巍.多尺度局部特征选择的行人重识别算法[J].计算机工程与应用，2020，56（2）：141-145.
XU J Z，LI T，YANG W.Person re-identification by multi-scale local feature selection[J].Computer Engineering and Applications，2020，56（2）：141-145.
[11] VARIOR R R，HALOI M，WANG G.Gated siamese convolutionalneural network architecture for human re-identification[C]//European Conference on Computer Vision，2016：791-808.
[12] HERMANS A，BEYER L，LEIBE B.In defense of the tripletloss for person re-identification[J].arXiv：1703. 07737，2017.
[13] CHEN W H，CHEN X T，ZHANG J G，et al.Beyond triplet loss：a deep quadruplet network for person re-identification[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu，Hawaii，USA：IEEE，2017：403-412.
[14] ZHENG Z D，ZHENG L，YANG Y.Pedestrian alignment network for large-scale person[J].arXiv：1707.00408，2017.
[15] SUN Y F，ZHENG L，YANG Y，et al.Beyond part models：person retrieval with refined part pooling[J].arXiv：1711. 09349，2017.
[16] 金翠，王洪元，陈首兵.基于随机擦除行人对齐网络的行人重识别方法[J].山东大学学报（工学版），2018，48（6）：67-73.
JIN C，WANG H Y，CHEN S B.Person re-identification based on random erasing pedestrian alignment network method[J].Journal of Shandong University（Engineering Science），2018，48（6）：67-73.
[17] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[18] WOO S，PARK J，LEE J Y，et al.Cbam：convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision，2018：3-19.
[19] SUN Y，ZHENG L，DENG W，et al.SVDNet for pedestrian retrieval[J].arXiv：1703.05693，2017.