Vehicle counting and speed measurement using headlight detection

Sina, Ibnu; Wibisono, Ari; Nurhadiyatna, Adi; Hardjono, Benny; Jatmiko, Wisnu; Mursanto, Petrus

doi:10.1109/icacsis.2013.6761567

Cited by 51 publications

(46 citation statements)

References 9 publications

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“…Existing solutions for traffic surveillance camera calibration (Dailey et al, 2000;Schoepflin and Dailey, 2003;Cathey and Dailey, 2005;Grammatikopoulos et al, 2005;He and Yung, 2007b;Maduro et al, 2008;Sina et al, 2013;Nurhadiyatna et al, 2013;Dubská et al, 2014;Lan et al, 2014;Luvizon et al, 2014;Dubská et al, 2015;Do et al, 2015;Luvizon et al, 2016;You and Zheng, 2016) (see Section 2 for detailed analysis) usually have limitations for real world applications. They are either limited to some viewpoints (zero pan, second vanishing point at infinity), or they require some per-installed-camera manual work.…”

Section: Introductionmentioning

confidence: 99%

Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement

Sochor

Jurnek

Herout

2017

Computer Vision and Image Understanding

103

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In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-ofthe-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50 % (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % -7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % -1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day).

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Section: Introductionmentioning

confidence: 99%

Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement

Sochor

Jurnek

Herout

2017

Computer Vision and Image Understanding

103

View full text Add to dashboard Cite

show abstract

“…Sina et al [11] focus on speed measurement at night. They used detected and paired headlights to detect vehicles, track them and measure their speed.…”

Section: Methods Using Manual Measurementsmentioning

confidence: 99%

Comprehensive Data Set for Automatic Single Camera Visual Speed Measurement

Sochor

Juránek

Špaňhel

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In this paper, we focus on traffic camera calibration and visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are hard to compare due to a lack of a common dataset with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what are the factors affecting their performance. We captured a new dataset of 18 full-HD videos, each around one hour long, captured at 6 different locations. Vehicles in the videos (20 865 instances in total) are annotated with precise speed measurements from optical gates using LIDAR and verified with several reference GPS tracks. We made the dataset available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, etc.) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this dataset in detail.

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“…There is an extensive literature on vehicle speed estimation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Most approaches are based on specialized speed sensors [6], or are dedicated to specific contexts, such as speed estimation from vehicle's headlight in night scenes [7,8], speed estimation using the signal from mobile telecommunication network [9], or speed estimation from a car-mounted camera to avoid collisions [10].…”

Section: Related Workmentioning

confidence: 99%

“…Most approaches are based on specialized speed sensors [6], or are dedicated to specific contexts, such as speed estimation from vehicle's headlight in night scenes [7,8], speed estimation using the signal from mobile telecommunication network [9], or speed estimation from a car-mounted camera to avoid collisions [10]. However, an exhaustive review of such methods is far beyond the scope of this thesis.…”

Section: Related Workmentioning

confidence: 99%