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摘要: 随着国家对社会公共安全的日益重视, 无重叠视域监控系统已大规模的普及.行人再识别任务通过匹配不同视域摄像机下的行人目标, 在当今环境下显得尤为重要.由于深度学习依赖大数据解决过拟合的特性, 针对当前视频行人再识别数据量较小和学习特征单一的问题, 我们提出了一种基于视频的改进行人再识别方法, 该方法通过生成对抗网络去生成视频帧序列来增加样本数量和加入了行人关节点的特征信息去提升模型效率.实验结果表明, 本文提出的改进方法可以有效地提高公开数据集的识别率, 在PRID2011, iLIDS-VID数据集上进行实验, Rank 1分别达到了80.2%和66.3 %.Abstract: As the government keeps attaching importance to public security, non-overlapping viewsheds surveillance systems have been deployed widely. It has become especially important to recognize pedestrian target through matching cameras with different viewsheds in nowadays. Deep learning relies on big data to solve overfitting. However, the current video-based person re-identification only has small data volume and homogeneous learning features. To solve this, we put forward a method to improve person re-identification based on the video. This method can increase the sample quantity by generating video frame sequence through generative adversarial network. It also adds the feature information of the pedestrian joints, which can improve the model efficiency. The experiment result shows that the modified method discussed in this paper can improve the recognition rate of public datasets effectively. In the experiments on PRID2011 and iLIDS-VID, Rank 1 attained 80.2 % and 66.3 %, respectively.
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Key words:
- Person re-identiflcation /
- deep learning /
- generative adversarial network(GAN) /
- human pose estimation
1) 本文责任编委 刘青山 -
图 2 生成对抗网络生成的视频帧序列(后5帧)
Fig. 2 A sequence of video frames generated by GAN (last five frames)
图 5 融合生成对抗网络和姿态估计算法网络结构
Fig. 5 The structure of integration of GAN and pose estimation algorithm
表 1 不同算法在PRID2011数据集上的识别率(%)
Table 1 Matching rates of different methods on the PRID2011 dataset (%)
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表 2 不同算法在PRID2011数据集上对识别率的影响(%)
Table 2 The influence of different methods on matching rates based on PRID2011 dataset (%)
方法 Rank 1 Rank 5 Rank 10 Rank 20 ASTPN 77.0 95.0 99.0 99.0 ASTPN+GAN 79.2 95.3 99.2 99.2 ASTPN+KeyPoint 78.6 95.1 99.1 99.1 本文方法 80.2 96.0 99.1 99.2
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表 3 不同算法在iLIDS-VID数据集上的识别率(%)
Table 3 Matching rates of different methods on the iLIDS-VID dataset (%)
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表 4 不同算法在iLIDS-VID数据集上对识别率的影响(%)
Table 4 The influence of different methods on matching rates based on iLIDS-VID dataset (%)
方法 Rank 1 Rank 5 Rank 10 Rank 20 ASTPN 62.0 86.0 94.0 98.0 ASTPN+GAN 64.4 87.5 95.1 98.0 ASTPN+KeyPoint 64.5 87.5 96.1 98.1 本文方法 66.3 88.4 96.2 98.1
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表 5 每个行人轨迹递归生成的图片张数$N$对PRID2011数据集上识别率的影响(%)
Table 5 The influence of the number $N$ of pictures generated recursively by each pedestrian trace on matching rate based on PRID2011 dataset (%)
$ N$ Rank 1 Rank 5 Rank 10 Rank 20 1 77.4 95.2 99.0 99.0 3 78.6 95.6 99.1 99.1 5 80.2 96.0 99.2 99.3 7 80.1 96.0 99.2 99.2 9 77.6 95.5 98.7 99.1 11 76.7 95.4 98.2 99.0
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表 6 每个行人轨迹递归生成的图片张数$N$对iLIDS-VID数据集上识别率的影响(%)
Table 6 The influence of the number $N$ of pictures generated recursively by each pedestrian trace on matching rate based on iLIDS-VID dataset (%)
$N $ Rank 1 Rank 5 Rank 10 Rank 20 1 61.9 86.7 94.2 97.8 3 63.1 87.5 95.6 98.0 5 66.3 88.4 96.2 98.1 7 66.0 88.4 96.0 98.1 9 64.8 87.9 94.3 97.9 11 64.6 86.6 94.1 97.6
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