Computer Science ›› 2019, Vol. 46 ›› Issue (11): 267-271.doi: 10.11896/jsjkx.181001861

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Person Re-identification Algorithm Based on Bidirectional KNN Ranking Optimization

BAO Zong-ming1, GONG Sheng-rong1,2, ZHONG Shan1,2, YAN Ran1, DAI Xing-hua1   

  1. (School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)1
    (School of Computer Science and Engineering,Changshu Institute of Technology,Suzhou,Jiangsu 215500,China)2
  • Received:2018-10-08 Online:2019-11-15 Published:2019-11-14

Abstract: The imaging factors such as illumination,view,obstruction and noise would bring great changes to pedes-trian’s appearance under the cross-view condition in person re-identification,then it becomes very difficult to identify the target from candidates.Using the re-ranking algorithm can optimize the re-identification’s result,but it can make the task time-consuming and expensive.What’s more,it is easy to introduce the noise during the process of re-ranking,which in turn affects the accuracy of re-identification.To solve the problem,this paper presented a re-ranking method based on bidirectional KNN for person re-identification.First,it utilized the pre-training and fine-tuning strategy to extract the deep features of pedestrian.Then,it choosed an appropriate metric function (XQDA,KISSME) to measure the distance of features.Finally,accor-ding to the bidirectional KNN relation between the query and candidates,the Jaccard distance was calculated and aggregated with the original distance to guide the re-ranking.Experiments on the datasets of CUHK03,Market1501 and PRW show that the re-ranking algorithm proposed in this paper can improve the accuracy of re-identification on the basis of the original method,and the improvements are 12.2% and 13.4% in the two evaluation indexes of Rank1 and mAP respectively.The experimental data indicates that the re-identification algorithm based on bidirectional KNN can effectively reduce the probability of noise during the re-ranking,and then improve the accuracy of re-identification.

Key words: Person re-identification, Re-ranking, Deep feature, Metric learning, Bidirectional KNN

CLC Number: 

  • TP391
[1] QI M B,WANG C C,JIANG J G,et al.Pedestrian re-identification with multi-feature fusion and alternating direction multiplier method[J].Chinese Journal of Image and Graphics,2018,23(6):827-836.(in Chinese)齐美彬,王慈淳,蒋建国,等.多特征融合与交替方向乘子法的行人再识别[J].中国图象图形学报,2018,23(6):827-836.
[2] YE M,LIANG C,YU Y,et al.Person Reidentification via Ranking Aggregation of Similarity Pulling and Dissimilarity Pushing[J].IEEE Transactions on Multimedia,2016,18(12):2553-2566.
[3] GAO Q Y,FANG H S.HOG Pedestrian Detection Algorithm of Multiple Convolution Feature Fusion[J].Computer Science,2017,44(S2):199-201.(in Chinese)高琦煜,方虎生.多卷积特征融合的HOG行人检测算法[J].计算机科学,2017,44(S2):199-201.
[4] GRAY D,TAO H.Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features[C]∥Proceedings of European Conference on Computer Vision.Marseille:Springer,2008:262-275.
[5] CHEN Y C,ZHENG W S,LAI J.Mirror representation formodeling view-specific transform in person re-identification[C]∥Proceedings of International Conference on Artificial Intelligence.Austin:AAAI Press,2015:3402-3408.
[6] LIAO S,HU Y,ZHU X,et al.Person Re-identification by Local Maximal Occurrence Representation and Metric Learning[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE Press,2015:2197-2206.
[7] DONG H,LU P,ZHONG S,et al.Person Re-identification byEnhanced Local Maximal Occurrence Representation and Genera-lized Similarity Metric Learning[J].Neurocomputing,2018,307:25-37.
[8] DONG H,GONG S,LIU C,et al.Large margin relative distance learning for person re-identification[J].IET Computer Vision,2017,11(6):455-462.
[9] YI D,LEI Z,LIAO S,et al.Deep Metric Learning for Person Reidentification[C]∥Proceedings of International Conference on Pattern Recognition.Stockholm:IEEE Press,2014:34-39.
[10] LI W,ZHAO R,XIAO T,et al.DeepReID:Deep Filter Pairing Neural Network for Person Re-identification[C]∥Proceedings of Computer Vision and Pattern Recognition.Columbus:IEEE Press,2014:152-159.
[11] XIAO T,LI H,OUYANG W,et al.Learning deep feature representations with domain guided dropout for person re-identification[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:1249-1258.
[12] CHEN Y C,ZHUX T,ZHENG W S,et al.Person Re-Identification by Camera Correlation Aware Feature Augmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(2):392-408.
[13] GARCIA J,MARTINEL N,GARDEL A,et al.DiscriminantContext Information Analysis for Post-Ranking Person Re-Identification[J].IEEE Transaction Image Processing,2017,26(4):1650-1665.
[14] LENG Q M,HU R M,LIANG C,et al.Person re-identification with content and context re-ranking[J].Multimedia Tools & Applications,2015,74(17):6989-7014.
[15] YE M,CHEN J,LENG Q M,et al.Coupled-View Based Ranking Optimization for Person Re-identification[C]∥Proceedings of International Conference on Multimedia Modeling.Sydney:Springer Press,2015:105-117.
[16] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Press,2016:770-778.
[17] FELZENSZWALB P,MCALLESTER D,RAMANAN D.A discriminatively trained,multiscale,deformable part model[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Anchorage:IEEE Press,2008:1-8.
[18] ZHENG L,YANG Y,HAUPTMANN A G.Person Re-identification:Past,Present and Future[J].arXiv:1610.02984.
[19] ZHENG L,ZHANG H,SUN S,et al.Person Re-identification in the Wild[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:3346-3355.
[20] SUN Y,ZHENG L,DENG W,et al.SVDNet for Pedestrian Retrieval[C]∥Proceedings of IEEE International Conference on Computer Vision.Venice:IEEE Press,2017:3820-3828.
[21] ZENG M,WU Z,TIAN C,et al.Person re-identification based on a novelmahalanobis distance feature dominated KISS metric learning[J].Electronics Letters,2016,52(14):1223-1225.
[22] ZHONG Z,ZHENG L,CAO D,et al.Re-ranking Person Re-identification with k-reciprocal Encoding[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE Press,2017:3652-3661.
[23] ZHANG X,LUO H,FAN X,et al.Aligned ReID:Surpassing Human-Level Performance in Person Re-Identification[J].ar-Xiv:1711.08184.
[24] BAI S,BAI X.Sparse Contextual Activation for Efficient Visual Re-Ranking[J].IEEE Transactions on Image Processing,2016,25(3):1056-1069.
[25] SARFRAZ M S,SCHUMSNN A,EBERLE A,et al.A PoseSensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:420-429.
[26] HERMANS A,BEYER L,LEIBE B.In defense of the tripletloss for person re-identification[J].arXiv:1703.07737.
[27] ZHONG Z,ZHENG L,ZHENG Z,et al.Camera Style Adaptation for Person Re-identification[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:5157-5166.
[28] ZHANG Y,XIANG T,HOSPEDALES T M,et al.Deep Mutual Learning[J].arXiv:1706.00384.
[29] HE L,LIANG J,LI H,et al.Deep Spatial Feature Reconstruction for Partial Person Re-identification:Alignment-Free Approach[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE Press,2018:7073-7082.
[1] PEI Jia-zhen, XU Zeng-chun, HU Ping. Person Re -identification Fusing Viewpoint Mechanism and Pose Estimation [J]. Computer Science, 2020, 47(6): 164-169.
[2] ZHOU Peng-cheng,GONG Sheng-rong,ZHONG Shan,BAO Zong-ming,DAI Xing-hua. Image Semantic Segmentation Based on Deep Feature Fusion [J]. Computer Science, 2020, 47(2): 126-134.
[3] LI Hao, TANG Min, LIN Jian-wu, ZHAO Yun-bo. Cross-modality Person Re-identification Framework Based on Improved Hard Triplet Loss [J]. Computer Science, 2020, 47(10): 180-186.
[4] ZHANG Yan-hong, ZHANG Chun-guang, ZHOU Xiang-zhen, WANG Yi-ou. Diverse Video Recommender Algorithm Based on Multi-property Fuzzy Aggregate of Items [J]. Computer Science, 2019, 46(8): 78-83.
[5] LI Yin-min, XUE Kai-xin, GAO Zan, XUE Yan-bin, XU Guang-ping, ZHANG Hua. 3-D Model Retrieval Algorithm Based on Residual Network [J]. Computer Science, 2019, 46(3): 148-153.
[6] YU Cheng, ZHU Wan-ning, YOU Kun, ZHU Jin-fu. Prediction Model of E-sports Behavior Pattern Based on Attention Mechanism and LRUA Module [J]. Computer Science, 2019, 46(11A): 76-79.
[7] WEN Jun-hao, DAI Da-wen, YU Jun-liang, GAO Min, ZHANG Yi-hao. Social Recommendation Method Integrating Matrix Factorization and Distance Metric Learning [J]. Computer Science, 2018, 45(10): 196-201.
[8] LIANG Lu, GONG Ben-long, LI Jian and TENG Shao-hua. Diffusion Method of Sample Points for Alleviating Staggered Situation of Classification [J]. Computer Science, 2017, 44(9): 286-289.
[9] WU Wei, GAO Guang-lai and NIE Jian-yun. Combination of Nearest Neighbor with Semantic Distance for Image Annotation [J]. Computer Science, 2015, 42(1): 297-302.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .