Towards speed detection camera system for a radar alternative

Ibrahim, Osman Ali Sadek; El-Gendy, Hazem; ElShafee, Ahmed

doi:10.1109/itst.2011.6060131

Cited by 8 publications

(14 citation statements)

References 1 publication

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“…Prior knowledge about the frame rate of the camera or accurate timestamps per each image are needed to compute the time between measurements. The use of consecutive [14, 18, 19, 22, 25, 33, 35–37, 41, 46, 48, 50, 55, 65, 67, 68, 71–76, 78, 82, 83, 85, 88, 89, 92, 96, 100, 103, 120, 129] or non‐consecutive [15, 24, 26, 28, 30, 31, 43, 44, 47, 49, 52, 59, 66, 70, 71, 86, 87, 93, 95, 99, 106–108, 111, 118, 124, 125, 128] images to estimate speed is a fundamental variable that has a considerable impact on accuracy. How to integrate all available measurements (instantaneous, mean, optimal etc.)…”

Section: Taxonomymentioning

confidence: 99%

“…The first categorization is directly related to the attempt to devise an accurate system to be used for speed enforcement ( speed camera application domain) [13, 17, 18, 32, 33, 39, 41–43, 51, 53, 54, 61, 65–68, 70, 72, 74, 76–78, 80, 82, 84–86, 94, 100–102, 107, 108, 111, 112, 114, 119, 122–125] or a less accurate approach that can still be used for traffic speed estimation ( traffic camera application scenario) [12, 14–16, 19–31, 34–38, 40, 44–50, 52, 55–60, 62–64, 69, 71, 73, 75, 79, 81, 83, 87–93, 95–99, 103–106, 109–111, 113, 115–118, 120, 121, 126–129]. Although in some cases it is claimed that accurate speed detection is targeted, some aspects such as the low resolution of the camera, a limited experimental evaluation or the absence of an appropriate ground truth make us to categorize these manuscripts as traffic speed systems.…”

Section: Taxonomymentioning

confidence: 99%

“…Since the cameras are mostly static (except for the drone cameras), in most cases, vehicle detection is addressed by modeling and subtracting the background [12–15, 18, 20–28, 30, 32, 34–37, 40, 42–44, 47, 48, 50, 51, 53–55, 61–63, 65, 71–75, 81, 83, 84, 86, 92, 93, 95, 102, 103, 108, 110–113, 117, 118, 121]. Other approaches are feature‐based, for example, detecting the license plate [31, 41, 49, 60, 66, 68, 70, 76, 77, 80, 82, 94, 122, 125] or other features of the vehicle [38, 39, 45, 49, 57–59, 69, 71, 79, 87, 91, 95, 96, 98, 99, 110–112, 116, 117, 123].…”

Section: Taxonomymentioning

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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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: Taxonomymentioning

confidence: 99%

Section: Taxonomymentioning

confidence: 99%

Section: Taxonomymentioning

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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“…This is not only for military applications but also for civilian applications. The latter includes (but not limited to) monitoring speeds of vehicles on high ways, sport competitions, aeroplanes, etc [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…In the literal [1], authors introduce the primary steps towards developing the Speed Detection Radar. Here authors introduce a new approach in object detection technique, which is "adaptive background subtraction" as it proofs that it's not sensitive to sudden illumination changes.…”

Section: Introductionmentioning

confidence: 99%

Speed Detection Camera System using Image Processing Techniques on Video Streams

Ibrahim¹,

El-Gendy²,

ElShafee³

2011

IJCEE

Self Cite

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Abstract-This paper, presents a new Speed Detection Camera System (SDCS) that is applicable as a radar alternative. SDCS uses several image processing techniques on video stream in online -captured from single camera-or offline mode, which makes SDCS capable of calculating the speed of moving objects avoiding the traditional radars' problems. SDCS offers an en-expensive alternative to traditional radars with the same accuracy or even better. SDCS processes can be divided into four successive phases; first phase is Objects detection phase. Which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction? The second phase is Objects tracking, which consists of three successive operations, Object segmentation, Object labelling, and Object canter extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like; Simple tracking, object has left the scene, object has entered the scene, object cross by another object, and object leaves and another one enters the scene. Third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass-by the scene. The final phase is Capturing Object's Picture phase, which captures the image of objects that violate the speed limits. SDCS is implemented and tested in many experiments; it proved to have achieved a satisfactory performance.

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