Abstract-Many vision-based automatic traffic monitoring systems require a calibrated camera for computing speeds and length-based classifications of tracked vehicles. A number of techniques, both manual and automatic, have been proposed for performing such calibration, but no study has yet focused upon evaluating the relative strengths of these different alternatives. We present a taxonomy for roadside camera calibration that not only encompasses the existing methods (VVW, VWH, and VWL) but also includes several novel ones as well (VVH, VVL, VLH, VVD, VWD, and VHD). We also introduce an overconstrained approach that takes into account all the available measurements, resulting in reduced error as well as overcoming the inherent ambiguity in the single-vanishing-point solutions. This important but oft-neglected ambiguity has not received the attention that it deserves; we analyze it and propose several ways of overcoming it. Our analysis includes the relative tradeoffs between twovanishing-point solutions, single-vanishing-point solutions, and solutions that require the distance to the road to be known. The various methods are compared using simulations and experiments with real images, showing that methods that use a known length generally outperform the others in terms of error, and that the overconstrained method reduces errors even further.
Vision-based automatic traffic monitoring systems require a calibrated camera in order to measure the speeds of tracked vehicles. Typically this calibration is done by hand. We present an automatic technique to calibrate the camera for typical viewpoints on highways using a realtime 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 upon 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.
2Because of a recent federal initiative, states are now required (as of June 2008) to collect and 3 submit motorcycle VMT data to the FHWA. These data are needed to obtain better counts of 4 motorcycles to evaluate their impact on crashes and traffic flow. However, there is concern 5 about the quality of data submitted. Many states have identified problems with using automatic 6 traffic recorders to account for motorcycle traffic. Existing sensors exhibit difficulties in 7 counting motorcycles that travel side by side or close behind each other, they have difficulty in 8 distinguishing larger motorcycles from passenger vehicles, and magnetic counters in particular 9 do not sense motorcycles that do not pass over or travel close enough to the sensor.
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