End-to-end Multi-Object Tracking Algorithm Integrating Global Local Feature Interaction and Angular Momentum Mechanism
A novel end-to-end algorithm is proposed to tackle the dependency of Multi-Object Tracking(MOT)algorithm performance on detection accuracy and data association strategies.Concerning detection,the Spatial Residual Feature Pyramid Network(SRFPN)is introduced based on feature pyramid networks to enhance feature fusion and information propagation efficiency.Subsequently,a Global Local Feature Interaction Module(GLFIM)is introduced to balance local details and global contextual information,thereby improving the focus of multi-scale feature outputs and the model's adaptability to target scale variations.Regarding the association,an Angular Momentum Mechanism(AMM)is introduced to consider target motion direction,thereby enhancing the accuracy of target matching between consecutive frames.Experimental validation on MOT17 and UAVDT datasets demonstrates significant enhancements in both detection and association performance of the proposed tracker,showcasing robustness in complex scenarios such as target occlusion,scale variation,and cluttered backgrounds.
Object trackingFeature Pyramid Network(FPN)Global local feature interactionsAngular momentum