基于雙向參考集矩陣度量學(xué)習(xí)的行人再識(shí)別
doi: 10.11999/JEIT190159
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江南大學(xué)輕工過程先進(jìn)控制教育部重點(diǎn)實(shí)驗(yàn)室 無錫 214000
基金項(xiàng)目: 國家自然科學(xué)基金(61573168)
Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set
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Key Laboratory of Advanced Control Light Process, Jiangnan University, Wuxi 214000, China
Funds: The National Natural Science Foundation of China (61573168)
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摘要:
針對(duì)行人再識(shí)別中由于外觀差異不顯著導(dǎo)致特征描述不準(zhǔn)確的問題，該文提出一種基于雙向參考集矩陣度量學(xué)習(xí)(BRM2L)的行人再識(shí)別算法。首先通過互近鄰算法獲得每個(gè)攝像頭下的互近鄰參考集，為保證參考集的魯棒性，聯(lián)合考慮各攝像頭下的互近鄰參考集獲得雙向參考集。通過雙向參考集挖掘出困難樣本進(jìn)行特征描述，從而得到準(zhǔn)確的外觀差異描述。最后利用該特征描述進(jìn)行更有效的矩陣度量學(xué)習(xí)。在多個(gè)公開數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果證明了該算法比現(xiàn)有算法具有更好的行人再識(shí)別性能。
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關(guān)鍵詞:
- 行人再識(shí)別 /
- 外觀差異 /
- 矩陣度量 /
- 互近鄰 /
- 雙向參考集
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個(gè)數(shù)據(jù)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 ${{\rm{L}}_{\rm{2}}}$范數(shù)(GoG) 19.00 38.00 24.17 51.33 32.44 60.00 F范數(shù)(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 ${{\rm{L}}_{\rm{2}}}$范數(shù)(FCTNN) 29.00 46.00 37.44 58.00 31.73 57.96 F范數(shù)(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數(shù)據(jù)集上的結(jié)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數(shù)據(jù)集上的結(jié)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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