集成全局局部特征交互与角动量机制的端到端多目标跟踪算法

End-to-end Multi-Object Tracking Algorithm Integrating Global Local Feature Interaction and Angular Momentum Mechanism

针对多目标跟踪(MOT)算法性能对于检测准确度和数据关联策略的依赖性问题,该文提出一种新的端到端算法。在检测方面,首先基于特征金字塔网络,提出空间残差特征金字塔模块(SRFPN),以提升特征融合和信息传递的效率。随后,引入全局局部特征交互模块(GLFIM)来平衡局部细节和全局上下文信息,增强多尺度特征的专注度,提高模型对目标尺度变化的适应性。在关联方面,引入角动量机制(AMM),充分考虑目标运动方向,以提升连续帧之间目标匹配的精确性。在MOT17和UAVDT数据集上进行实验验证,所提跟踪器的检测性能和关联性能均显著提升,并且在目标遮挡、尺度变化和杂乱背景等复杂场景下表现出良好的鲁棒性。

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 MOT17and 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.