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Conference Paper
Infrastructure-Assisted Defensive Forecasting of Latent Traffic Hazards from Uncalibrated CCTV
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- Authors
- Je-Seok Ham, Hyunseo Lee, Yongseon Lee, Kwanyong Park, Jinyoung Moon, Changick Kim
- Issue Date
- 2026-09
- Citation
- European Conference on Computer Vision (ECCV) 2026 Workshop : Safe and Defensive Autonomous Driving (SDAD), pp.1-9
- Publisher
- Safe and Defensive Autonomous Driving (SDAD)
- Language
- English
- Type
- Conference Paper
- Abstract
- Safe autonomous driving requires anticipating potentially hazardous vehicle motions beyond the limited field of view of ego-centric sensors. Existing urban CCTV cameras provide a complementary infrastructure-side perspective, but their use for motion forecasting is hindered by unknown camera parameters, perspective distortion, and noisy vehicle tracks. To address these challenges, we present NAD-Traj, an infrastructure-assisted trajectory forecasting framework designed for uncalibrated urban CCTV. We introduce a calibration-free vehicle trajectory extraction pipeline and a robust graph-based prediction model equipped with a GRU-based temporal refinement module and a noise-aware loss function. Using this pipeline, we construct NAD-Traj-DB, containing 7,364 traffic scenes, over 528K vehicle-track instances, and 21.4M trajectory points from urban intersections. Experimental results on NAD-Traj-DB and the public V2X-Seq benchmark demonstrate that our approach reduces the Miss Rate (MR) by 23.6% relative to the strongest evaluated baseline. These results highlight the potential of uncalibrated urban CCTV to serve as a promising infrastructure-side perception and forecasting framework for future defensive-driving systems.
- KSP Keywords
- Calibration-free, Field Of View(FOV), Graph-based, Loss Function, Miss rate, Perspective Distortion, Trajectory forecasting, Unknown camera parameters, Vehicle trajectory, autonomous driving, forecasting framework