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