Cross-modal person re-identification method based on heterogeneous information alignment and reranking
Cross-modal person re-identification between visible and infrared light images is a challenge due to the differences in imaging principles.The alignment and mining of heterogeneous pedestrian information become difficult.To address this,we proposed a cross-modal person re-identification method based on heterogeneous information alignment and reranking.The proposed method includes a new algorithm for heterogeneous local information alignment,which dynamically matches the same pedestrian heterogeneous local information by obtaining the shortest path of the distance matrix.We also proposed an extended k-nearest neighbor reranking algorithm,which realizes the same pedestrian heterogeneous information fusion and reduces the difficulty of information mining by dynamically expanding the heterogeneous information of the query image's k mutual nearest neighbors.The experimental results show that our method improves mAP and Rank-1 evaluation indexes by 10.12%and 8.6%respectively on the SYSU dataset compared to the AGW model combined with k mutual nearest neighbor reranking algorithm.
cross-modalperson re-identificationheterogeneous information alignmentrerankingdeep learning
NETL
NSTL
万方数据
