Monocular 3D Vehicle Detection Based on Video Point Tracking: A Novel Approach Without Prior Information

Monocular vision systems have emerged as a promising solution for 3D object detection due to their cost-effectiveness and deployment simplicity. However, existing methods heavily rely on prior information such as camera parameters and complex 3D dataset annotations. Most current approaches focus on static RGB images, struggling to recover 3D information from 2D inputs. We propose a novel monocular 3D vehicle detection method based on point tracking from a roadside perspective, leveraging dynamic inter-frame associations in video data. Our method operates without prior data like vehicle dimensions or camera parameters, and eliminates the need for complex 3D dataset construction and annotation. It integrates object detection and semantic segmentation for feature extraction, high-precision inter-frame data association, and trajectory-based state analysis. 3D reconstruction is achieved through reference point determination and coordinate calibration under geometric constraints. Experimental results on the DAIR-V2X-I dataset demonstrate superior performance over baseline models, with error reductions ranging from 1.3% to 7.3% across various metrics (A3DS, A2DS, AGS, ALS, APS, AW). This approach presents a practical solution for monocular 3D object detection while eliminating dependency on prior information and complex datasets.