Vehicle speed estimation using extracted SURF features from stereo images

Bouziady, Abderrahim El; Thami, Rachid Oulad Haj; Ghogho, Mounir; Bourja, Omar; Fkihi, Sanaa El

doi:10.1109/isacv.2018.8354040

Cited by 27 publications

(36 citation statements)

References 13 publications

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“…El Bouziady et al [11] use a horizontal stereo laboratory pre-calibrated setup. After background subtraction, vehicles are detected and tracked as convex blobs.…”

Section: Related Workmentioning

confidence: 99%

“…Of the remaining points, the one that is closest to the centre of the license plate is considered as the exact spatial location of the target vehicle in the current stereo frame pair. Using the spatial locations of the vehicle in two frames they compute the average speed in the same fashion as in previous works [9], [11]. They compared the measured speed with the ground truth obtained from a professional satellite speed meter.…”

Section: Related Workmentioning

confidence: 99%

“…The mean absolute percentage error is 0.23 % and maximum percentage error is 1.11 %. We compare the speed measurement errors with three other stereo-based vehicle speed measurement methods and one two-camera method mentioned in chapter II, namely, Jalalat et al's method [9], El Bouziady et al's method [11], Yang et al's method [12], and Llorca et al's [14] method. The comparison is shown in Table II.…”

Section: E Speed Measurementmentioning

confidence: 99%

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Vehicle Speed Measurement Using Stereo Camera Pair

Najman

Zemčík

2022

IEEE Trans. Intell. Transport. Syst.

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“…El Bouziady et al [11] use a horizontal stereo laboratory pre-calibrated setup. After background subtraction, vehicles are detected and tracked as convex blobs.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: E Speed Measurementmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Speed Measurement Using Stereo Camera Pair

Najman

Zemčík

2022

IEEE Trans. Intell. Transport. Syst.

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“…Measuring distances in real-world coordinates is the most critical problem to be solved for accurate vehicle speed estimation. This task is straightforward when using stereo vision [42,60,61,74,80,92,100,112]. For each detected vehicle, relative distances can be directly obtained using the disparity values of the pixels contained in the region of the vehicle.…”

Section: Stereo-based Approachesmentioning

confidence: 99%

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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“…Stereo-vision has received much attention in the last few years in the area of transportation. It can be used for speed estimation [2], 3D vehicle reconstruction [3], Vehicle detection [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

Derrouz¹,

Elbouziady²,

Abdelali³

et al. 2019

IEEE Access

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Vehicle type classification is a critical function in any intelligent transportation system (ITS). In this paper, we present a novel two-layer vehicle type classification framework based on the vehicle's 3D parameters and its local features. This framework is a part of the first Moroccan video intelligent transport system (MOVITS) that aims to control traffic and road code violations. In the first layer, the 3D features are extracted using the disparity map generated from stereo-images, and then, the width, height, and length of the vehicle are calculated based on the obtained list of 3D points. In the second layer, a gradient-based method is applied to extract the 2D features, and a dimensional reduction algorithm is performed to reduce its size. Both features are combined to construct the final feature vector that is used as an input for the classification. The Moroccan dataset and the BIT dataset were used to, respectively, validate the proposed framework and conduct a comparative study with the state-of-the-art algorithms. The experimental results demonstrate the efficiency of our approach against existing algorithms.

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Vehicle speed estimation using extracted SURF features from stereo images

Cited by 27 publications

References 13 publications

Vehicle Speed Measurement Using Stereo Camera Pair

Vehicle Speed Measurement Using Stereo Camera Pair

Vision‐based vehicle speed estimation: A survey

Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

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