Cross modal pedestrian detection on campus security platform based on graph related attention features
In order to explore a new campus safety monitoring mode,a cross modal pedestrian detection method based on graph associated attention features is proposed.This method integrates multimodal data and utilizes attention mechanisms and mask guidance to enhance the model's generalization ability.In indoor search mode,when the Rank is 20,compared to only inputting complete ima-ges,using both foreground and complete images as inputs can improve the recognition rate of the network by 6.7%.Compared to sepa-rate channel attention modules and spatial attention modules,the simultaneous use of both can improve the recognition rate of the net-work by 3.1%.The recognition rate of pedestrian detection on the improved campus safety platform is higher than 93%,and the accu-racy of pedestrian trajectory prediction is 97.8%.Compared to the pre improved campus security platform,the overall performance satisfaction evaluation of the improved campus security platform has increased by 31.2%.The cross modal pedestrian detection method proposed in the study has improved the accuracy and reliability of pedestrian detection on campus.This helps to ensure campus safety,prevent and reduce the occurrence of safety accidents.