Automatic Camera Calibration Using Pattern Detection for Vision-Based Speed Sensing

Abstract

Vision-based automatic traffic monitoring systems require a calibrated camera to measure the speeds of tracked vehicles. Typically this calibration is done by hand. An automatic technique was developed to calibrate the camera for typical viewpoints on highways using a real-time boosted cascade vehicle detector (BCVD). Image processing is used to estimate the two vanishing points, from which the camera height, focal length, and pan and tilt angles are calculated. The key contribution of the proposed approach is its applicability to a wide variety of environmental conditions. The technique does not rely on background subtraction, nor does it require scene-specific features such as pavement markings. As a result, it is unaffected by the presence of shadows, adverse weather conditions, headlight reflections, lack of ambient light, or spillover caused by low-mounted cameras. Speed estimation within 10% of ground truth is shown for sequences obtained during daylight, nighttime, and rain, and including shadows and severe occlusion.