The purpose of cross-modal pedestrian recognition from visible to infrared light is to realize the identification and judgment of pedestrian identity in day and night environments

which is of great research value in the field of video surveillance. Due to the different imaging principles of visible light and infrared light

cross-modal recognition is a challenge. A new network structure is designed to alleviate the data difference between modals and improve the accuracy of pedestrian recognition model. The proposed network structure is divided into two parts: attention-based modal transfer module embedded in the input stage of the feature network

which can reduce the difference across modals

and block-based multi-granularity feature decomposition module

which can consider both global information and local information and improve the utilization rate of effective information. The experimental results on the open data set SYSU-MM01 show that

the rank 1 of the cumulative matching characteristic( CMC) index and the mean average precision( m AP) index of the proposed method reaches 56. 45%and 53. 52%

respectively

which are 6. 53% and 2. 79% higher than the current best method( XIV

AAAI-2020)

and effectively improves the performance of cross-modal pedestrian recognition from visible to infrared light.