Continuous Full-Domain Highway Trajectory Tracking Based on Improved Deep-SORT and Inverse Covariance Intersection

Continuous full-domain vehicle trajectories are essential for smart highway monitoring, but single-sensor roadside perception is limited by physical coverage, occlusion, and environmental sensitivity. To address continuous trajectory tracking across multiple roadside-sensing domains, this study proposes a real-time, full-domain highway trajectory tracking framework based on radar-camera fusion, improved Deep-SORT, and inverse covariance intersection. At the local perception level, a two-stage object-level and decision-level fusion model is constructed, and Deep-SORT is improved using a CIoU matching strategy and an occluded target tracking controller to enhance local multi-object tracking continuity. At the cross-domain association level, a geometry-motion consistency stepwise calibration method is developed to unify adjacent sensing domains, and a CATS-ICI trajectory stitching strategy is introduced to improve trajectory association and state smoothness during sensor handover. The proposed framework was validated on a real highway test section with roadside radar, video, and drone-based ground-truth trajectories. Experimental results show that the full local method achieves an EMOTA of 92.35%, and the reconstructed full-domain trajectories achieve a successful trajectory matching rate of 98.4% under the 452 vehicles/10 min test condition. Additional ablation experiments further verify the contributions of radar-camera fusion, CIoU, OTTC, GMCSC, CATS, and ICI. These results demonstrate that the proposed framework can provide continuous and reliable full-domain vehicle trajectories for real-world highway monitoring.