Dynamic camera calibration of roadside traffic management cameras

Schoepflin, T.N.; Dailey, Daniel J.

doi:10.1109/itsc.2002.1041183

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Finally, studies on automated landmark identification ( 46 ) make it possible to retrieve objects from traffic images that can be used for georeferencing. A fusion of these research outcomes is promising not only to improve the accuracy of automatic georeferencing, but also to automatically filter out the image regions where vehicles appear to be too small to be detected ( 47 ). This will significantly improve the automation of the applications on pan-tilt-zoom cameras.…”

Section: Discussionmentioning

confidence: 99%

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

Lin

2022

Transportation Research Record: Journal of the Transportation R

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The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge because of the limitations of current vehicle detection techniques, as well as the various camera conditions, such as height and resolution. In this work, a quadtree-based algorithm is developed to continuously partition the image extent until only regions with high detection accuracy remain. These regions are referred to as high-accuracy identification regions (HAIRs) in this paper. We demonstrate how the use of HAIRs can improve the accuracy of traffic density estimates using images from traffic cameras at different heights and resolutions in Central Ohio. Our experiments show that the proposed algorithm can be used to derive robust HAIRs in which vehicle detection accuracy is 41% higher than that in the original image extent. The use of HAIRs also significantly improves the traffic density estimation with an overall decrease of 49% in root mean squared error.

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Section: Discussionmentioning

confidence: 99%

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

Lin

2022

Transportation Research Record: Journal of the Transportation R

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“…By using the techniques of the previous section to obtain K, the signal a(v) is warped into b( y) by using Equation 6, and a cubic polynomial resampling is applied to b( y) to obtain uniform sampling through the range of y. A typical result is contained in Figure 7b, where the values for S and φ are estimated by using a methodology outlined earlier (14). If tall vehicles from an adjacent lane noticeably affected the feature vector, one would expect to find two peaks in R t,t+1 [ y R ].…”

Section: Applying Equation 1 Yieldsmentioning

confidence: 99%

Correlation Technique for Estimating Traffic Speed from Cameras

Schoepflin

Dailey

2003

Transportation Research Record: Journal of the Transportation R

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A new algorithm is presented for estimating speed from roadside cameras in uncongested traffic, congested traffic, favorable weather conditions, and adverse weather conditions. Individual vehicle lanes are identified and horizontal vehicle features are emphasized by using a gradient operator. The features are projected into a one-dimensional subspace and transformed into a linear coordinate system by using a simple camera model. A correlation technique is used to summarize the movement of features through a group of images and estimate mean speed for each lane of vehicles.

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“…These methods are simple and fast, but not flexible as they require manual settings by an operator. Tracking-based approaches, Generally, consider three steps: vehicle detection [17], vehicle tracking and traffic parameters calculation [18], [4], [2], [9], [10], [11]. In these approaches, the images of individual cars need to be separated, which makes their applicability not feasible in real situations with changes in lighting conditions, traffic congestion and vehicle occlusion [7].…”

Section: Introductionmentioning

confidence: 99%

A High Efficient System for Traffic Mean Speed Estimation from MPEG Video

Sahli

Dong

et al. 2009

2009 International Conference on Artificial Intelligence and Computational Intelligence

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In this paper, we present a vision-based traffic measurement system, allowing automatic traffic flow segmentation, camera calibration and traffic information estimation. The system quickly estimates mean vehicle speed directly from MPEG Motion Vectors. Although extensive work has been done in extracting and using motion information from MPEG video data in compressed domain, to our best knowledge, only few works have been dedicated to the use of MPEG motion vector for traffic analysis. The proposed system is stable and handles camera vibrations and illumination changes. The paper describes the main principles of our system together with qualitative and quantitative representative results.

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Dynamic camera calibration of roadside traffic management cameras

Abstract: Ab8lrocl-In this paper we present a new algorithm to calibrate roadside traflic management cameras to create a lrafIic speed sensor. We present D simpUfied camera model (bat can produce g o d single vehicle s p e d cstlmates.Iridcx rernix-Vidm, speed sensor, camera calibration.

Cited by 11 publications

References 12 publications

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

Identifying High-accuracy Regions in Traffic Camera Images to Enhance the Estimation of Road Traffic Metrics: A Quadtree-based Method

Correlation Technique for Estimating Traffic Speed from Cameras

A High Efficient System for Traffic Mean Speed Estimation from MPEG Video

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