Road-Constrained Vehicle Trajectory Recovery from Traffic Video Using Spatio-Temporal Voxel Representation

Abstract

Road-constrained trajectory recovery from traffic surveillance videos has become a critical component in applications ranging from intelligent transportation to urban planning. Existing approaches typically perform explicit camera-road matching, which either relies on labor-intensive camera calibration or suffers from matching errors. Moreover, prior methods often represent trajectories as sequences of tuples (e.g., node or edge with timestamps), which limits effective spatio-temporal mining, leading to suboptimal recovery accuracy. To address these problems, we propose a novel trajectory recovery framework based on spatio-temporal voxel representation. The real-world coordinates of trajectory nodes are encoded as voxel indices, which naturally capture spatio-temporal relationships. Our framework leverages this voxel representation to enable direct road-constrained trajectory recovery from camera observations, eliminating the need for explicit heuristic camera-road matching. Building upon this voxel formulation, we further present a shape-aware evaluation scheme that assesses trajectories within a spatio-temporal voxel grid, capturing edge and structural information to provide a complementary perspective to existing evaluation metrics. Finally, extensive experiments across multiple datasets demonstrate that our method achieves state-of-the-art trajectory recovery accuracy, with an average F1-score improvement of 14.7%.