Vehicle Following Distance Violation Detection with City Surveillance Cameras

Maintaining an adequate following distance reduces the risk of rear-end collisions, but monitoring it with fixed urban surveillance cameras requires image observations to be converted into reliable metric quantities. This study presents and validates a measurement pipeline for following-distance violations from a single fixed camera. Vehicles are detected with YOLO11 and tracked with ByteTrack, BoT-SORT or OC-SORT; the bottom-centre of each bounding box is mapped to a longitudinal road coordinate by a per-lane homography whose along-road scale is calibrated once from loop-meter speeds. Vehicle speed, time headway, front-to-front spacing and time to collision are estimated from the trajectories, and eight violation criteria, including the rule that the distance in metres must not fall below half the speed in km/h, are evaluated together with a probabilistic estimate of decision uncertainty. On 14 public test videos (178 min, 5939 vehicles) with inductive-loop reference speeds, the best configuration (YOLO11-m with BoT-SORT) measured speed with a mean absolute error of 1.04 km/h, time headway with 0.057 s and spacing with 1.14 m, and detected half-speed-rule and 1.8 s headway violations with F1 scores of 0.81 and 0.85; with a headway error model estimated on the calibration video only, 4.5% of the vehicle pairs were identified as uncertain. On the UA-DETRAC benchmark, BoT-SORT achieved the highest HOTA (0.681) and IDF1 (0.790), consistent with its highest pair recall. Headway-based criteria were the most reliable, and the metric scale had to be calibrated on site rather than inferred from road dimensions.