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
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