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摘要: 對深度學習在行人重識別領域的應用現狀進行總結與評價。首先，對行人重識別進行介紹，包括行人重識別的應用場景、數據集與評價指標，并對基于深度學習的行人重識別的基本方法進行總結。之后，針對行人重識別的研究現狀，將近年來國內外學者的研究工作歸納為基于局部特征、基于生成對抗網絡、基于視頻以及基于重排序4個方向，并對每個方向所使用的方法分別進行梳理、性能對比以及總結。最后，對行人重識別領域現存的問題進行了分析與討論，并探討了行人重識別未來的發展方向。Abstract: Person re-identification is an important part of multi-target tracking across cameras; its aim is to identify the same person across different cameras. Given a query image, the purpose of person re-identification is to find the best match for the query image in an image set. Person re-identification is a key component in an intelligent security system; it is beneficial for building a smart bank or smart factory and plays a crucial role in the construction of a smart city. Nowadays, with the development of artificial intelligence and increasing demand for precise identification in practical scenarios, deep learning-based person re-identification technology has become a popular research topic; this technology has achieved state-of-the-art results in comparison with conventional approaches. Although there are many recently proposed networks with stronger representation ability and a high level of accuracy for person re-identification, there also exist some problems that should be considered and solved. These include the insufficient generalization ability of various poses, the inability to fully utilize the temporal information, and the ineffective identification of occluded objects. As a result, many scholars have researched this field and have pointed out some promising solutions to cope with the aforementioned problems. This paper aims to summarize the application of deep learning in the field of person re-identification along with its advantages and shortcomings. First, the background of person re-identification is introduced, including the application scenarios, datasets, and evaluation indicators. Additionally, some basic methods of person re-identification based on deep learning are summarized. According to the existing research on person re-identification, the main approaches proposed by scholars worldwide can be summarized into four aspects, which are based on local features, generative adversarial networks, video data, and re-ranking. A detailed comparative study of these four methods is then conducted. Finally, the existing problems and future studies that can be done in the field of person re-identification are analyzed and discussed.
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Key words:
- deep learning /
- person re-identification /
- local feature /
- generating adversarial networks /
- video data /
- reranking
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圖 1 行人重識別的應用場景示例
Figure 1. An example of person re-identification application scenarios
圖 2 基于深度學習的行人重識別研究問題與方法歸納
Figure 2. Research problems and methods of person re-identification based on deep learning
圖 3 行人重識別研究方法框架
Figure 3. Research method framework of person re-identification methods
表 3 各數據集的性能最優模型以及精度數據
Table 3. State-of-the-art
models and their precision for each dataset Dataset SOTA Rank-1 accuracy mAP Market-1501[4] St-ReID(RE, RK)[20] 97.20 86.70 Viewpoint-Aware Loss[21] 96.79 95.43 DG-Net[22] 94.80 84.00 DukeMTMC-reID[5] St-ReID(RE, RK, Cam)[20] 94.50 92.70 ABD-Net(ResNet-50)[23] 89.00 78.95 Viewpoint-Aware Loss[21] 93.90 91.80 CUHK03[3,6] FD-GAN[19] 92.60 91.30 OSNet[24] 67.80 — DG-Net[22] 61.10 — MSMT17[9] ABD-Net(ResNet-50)[23] 82.30 60.80 OSNet[24] 78.70 52.90 DG-Net[22] 77.20 52.30 
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表 4 基于局部特征的行人重識別方法的性能表現
Table 4. Performance of person re-identification method based on local feature
Methods Year Market-1501 DukeMTMC-reID CUHK03 Rank-1 mAP Rank-1 mAP Rank-1 mAP PCB+RPP[46] 2018 93.8 81.6 83.3 69.2 63.7 57.5 SPReID+re-ranking[48] 2018 94.6 90.9 88.9 84.9 — — RNLSTMA[54] 2019 90.3 76.4 77.0 62.1 86.1 83.6 mGD+ RNLSTMA[56] 2020 91.3 77.9 80.8 63.9 88.0 84.2 SMC-ReID[49] 2020 95.3 93.0 — — — — HOReID[53] 2020 94.2 84.9 86.9 75.6 — — ISP[50] 2020 95.3 88.6 89.6 80.0 — —
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表 5 基于生成對抗網絡的行人重識別方法的性能表現
Table 5. Performance of person re-identification method based on GAN
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表 6 基于視頻的的行人重識別方法的性能表現
Table 6. Performance of video-based person re-identification method
Methods Year Rank-1 MARS DukeMTMC VideoReID[15] PRID2011[7] iLIDS-VID[8] Rank-1 mAP Rank-1 mAP CNN+RNN+Temporal Pooling[77] 2016 70.0 58.0 — — Deep RCN[78]+KISSME 2016 69.0 46.1 RFA-Net[80] 2016 58.2 49.3 SCAN+ResNet50[81] 2018 92.0 81.3 86.6 76.7 ResNet3D-50+Non-Local[82] 2018 91.2 81.3 84.3 77.0 Spatial Attention+Temporal Attention[83] 2018 93.2 80.2 82.3 65.8 AP3D[84] 2020 — 86.7 90.1 85.1 96.3 95.6 STCNet[86] 2020 — 83.4 88.5 82.3 95.0 93.5 MGH[88] 2020 94.8 85.6 90.0 85.8
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