Camera calibration and near-view vehicle speed estimation

Futang, Peng; Liu, Changsong; Ding, Xiaoqing

doi:10.1117/12.765077

Cited by 4 publications

(23 citation statements)

References 13 publications

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“…For monocular systems, the estimation of the distance of the vehicles is usually computed with a set of constraints such as flat road assumption, including methods based on the homography [15,16,18,20,22,34,37,52,53,61,66,94,98,99,110] and the use of augmented intrusion lines, patterns or regions [19,20,23,24,26,32,38,47,79,86,87,107,124], or by using prior knowledge about the actual dimensions of some of the objects (e.g., the license plates [41,49,60,66,68,80,125] or the size of the vehicles [42,62,78,103]). These restrictions are alleviated when using stereo vision [42,60,61,74,80,92,100,112].…”

Section: Distance Estimationmentioning

confidence: 99%

“…In a few cases, the problem of detecting the speed of vehicles is posed as a problem of detecting the speed of traffic on a segment of the road, i.e. directly obtaining an average road speed value [12,17,20,23,32,34,38,40,42,51,53,54,58,61,69,77,79,80,81,84,85,94,102,105,110,113,121]. However, in most cases the detection is carried out at the level of individual vehicles.…”

Section: Speed Estimationmentioning

confidence: 99%

“…Most works use a low-medium focal length (≤ 25mm) since they are designed to cover multiple lanes and a large road stretch. Only a few works implicitly or explicitly highlight the need for a long focal length to increase the accuracy of the speed estimation, even if only one lane is covered by the system [23,31,53,61,68,70,76,77,80,82,107,124].…”

Section: Camera Settingsmentioning

confidence: 99%

“…5.c): obtained with high camera resolution, high focal lengths, low camera height, covering a short road segment, one or two lanes and only one direction. Some examples are [23,31,53,68,70,77,80,107,124]. Both intrinsic and extrinsic parameters are computed on site.…”

Section: Camera Settingsmentioning

confidence: 99%

“…The most common approach to perform vehicle detection is based on background subtraction [ 121], followed by some morphological operations and a blob analysis method. Different methodologies are used to perform background subtraction, including gray-and color-based approaches, Gaussian Mixture Models [23,61,84,102], adaptive background modeling [51,111], etc. In some cases, frame-by-frame or background subtraction is combined with some feature detector (e.g., edges, corners, or KLT features) [40,48,50,71,110].…”

Section: Static Backgroundmentioning

confidence: 99%

See 4 more Smart Citations

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.

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Section: Distance Estimationmentioning

confidence: 99%

Section: Speed Estimationmentioning

confidence: 99%

Section: Camera Settingsmentioning

confidence: 99%

Section: Camera Settingsmentioning

confidence: 99%

Section: Static Backgroundmentioning

confidence: 99%

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Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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Multi‐camera traffic scene mosaic based on camera calibration

Song

Dai

et al. 2021

IET Computer Vision

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Recently, the traffic application based on vision in the single traffic‐monitoring scene has been widely studied and developed. However, cross‐regional research is still in its infancy. In order to help solve the application of cross‐regional traffic surveillance scenarios, this paper proposes a more reliable and accurate road scene mosaic method under multi‐camera surveillance. The mosaic road panorama contains physical information, which can be used to achieve a cross‐regional measurement. It also lays the foundation for vehicle spatial location, vehicle speed and traffic incident detection across regions. First, the mapping relationship between the three‐dimensional sub‐world coordinates and their corresponding two‐dimensional image coordinates is established by camera calibration. Second, the projection transformation relationship between two cameras is established by two sub‐world coordinate systems and their common information. Finally, we use the proposed inverse projection idea and translation vector relationship to complete the mosaic of two traffic‐monitoring road scenes. The experimental results show that the camera calibration accuracy can reach more than 97% in a single scene. The measurement accuracy of the mosaic block is over 95%. The experimental results show that the proposed method has a higher accuracy, which has great value in related theoretical research and practical applications.

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