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.
关键词
目标跟踪/特征金字塔网络/全局局部特征交互/角动量
Key words
Object tracking/Feature Pyramid Network(FPN)/Global local feature interactions/Angular momentum
维普
万方数据