Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set
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摘要: 针对行人再识别中由于外观差异不显著导致特征描述不准确的问题，该文提出一种基于双向参考集矩阵度量学习(BRM2L)的行人再识别算法。首先通过互近邻算法获得每个摄像头下的互近邻参考集，为保证参考集的鲁棒性，联合考虑各摄像头下的互近邻参考集获得双向参考集。通过双向参考集挖掘出困难样本进行特征描述，从而得到准确的外观差异描述。最后利用该特征描述进行更有效的矩阵度量学习。在多个公开数据集上的实验结果证明了该算法比现有算法具有更好的行人再识别性能。Abstract: To solve the problem of inaccurate feature representation caused by indistinctive appearance difference in person re-identification domain, a new Matrix Metric Learning algerithm based on Bidirectional Reference (BRM2L) set is proposed. Firstly, reciprocal-neighbor reference sets in different camera views are respectively constructed by the reciprocal-neighbor scheme. To ensure the robustness of reference sets, the reference sets in different camera views are jointly considered to generate the Bidirectional Reference Set (BRS). With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained. Finally, these representations are utilized to conduct more effective matrix metric learning. Experimental results on several public datasets demonstrate the superiority of the proposed method.
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表 2 在3个数据集上采用不同特征的匹配精度(%)
方法 VIPeR CHUK01 PRID450S Rank-1 Rank-5 Rank-1 Rank-5 Rank-1 Rank-5 ${{\rm{L}}_{\rm{2}}}$范数(GoG) 19.00 38.00 24.17 51.33 32.44 60.00 F范数(GoG) 20.17 41.83 34.50 69.83 52.22 80.22 ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(GoG) 38.33 69.17 45.33 70.50 54.44 80.67 ${{\rm{L}}_{\rm{2}}}$范数(FCTNN) 29.00 46.00 37.44 58.00 31.73 57.96 F范数(FCTNN) 30.00 49.83 46.56 72.11 44.40 72.84 ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(FCTNN) 41.33 68.17 47.42 77.44 45.51 72.96 
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表 3 VIPeR数据集上的结果
方法 Rank-1 Rank-5 Rank-10 Rank-20 PCCA[16] 19.3 48.9 64.9 80.3 KISSME[18] 19.6 48.0 62.2 77.0 BiCov[17] 20.6 43.2 56.1 68.0 eSDC[19] 26.3 46.4 58.6 72.8 DeepMetric[24] 28.2 59.3 73.4 86.4 Midfilter[21] 29.1 52.5 65.9 79.9 LADF[20] 30.0 64.0 80.0 92.0 FTCNN[15]+XQDA 31.2 59.8 74.0 83.5 RD[6] 33.3 65.1 78.3 88.5 GoG[14]+XQDA 37.3 67.4 77.2 89.6 SCNCD[22] 37.8 68.5 81.2 90.4 ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4] 38.3 67.2 77.0 89.3 DeepRanking[25] 38.4 69.2 81.3 90.4 LOMO+XQDA[23] 40.0 68.5 80.5 91.0 DeepList[26] 40.5 69.1 80.1 91.2 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG) 38.33 69.17 81.50 89.50 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN) 41.33 68.17 82.00 90.33
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表 5 CUHK01数据集上的结果
方法 Rank-1 Rank-5 Rank-10 Rank-20 SDALF[1] 9.9 22.6 30.3 41.0 TML[12] 20.0 43.5 56.0 69.3 MidFilter[21] 34.3 55.1 65.0 74.9 ImprovedDeep[31] 47.5 71.0 80.0 – RD[6] 31.1 – 68.5 79.1 ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4] 43.7 70.1 77.4 88.7 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG) 45.33 70.50 86.50 90.00 ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN) 47.42 77.44 88.33 98.33
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