Single camera vehicles speed measurement

Dehghani, Abbas; Pourmohammad, Ali

doi:10.1109/iranianmvip.2013.6779976

Cited by 9 publications

(18 citation statements)

References 4 publications

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“…To get the classification remarked above, the lineal speed of the tracking element is estimated, which allows us to analyse the driver’s behaviour when the warning signal is on and to get a statistical analysis of the traffic in that section of the road, between other possibilities. Numerous works can be found that presents different methods to estimate the vehicles speed with only one camera [ 52 , 53 , 54 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

González-de-Soto

Mora

Martín-Jiménez

et al. 2020

Sensors

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A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

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Section: Methodsmentioning

confidence: 99%

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

González-de-Soto

Mora

Martín-Jiménez

et al. 2020

Sensors

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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%

“…We can even find a considerable number of works applying the simplest frame‐by‐frame approach followed by a thresholding method to perform image segmentation [14, 32, 34, 36, 37, 40, 42, 46, 54, 73, 103]. The most common approach to perform vehicle detection is based on background subtraction [12, 13, 15, 18, 20–28, 30, 36, 37, 43, 44, 47, 48, 50, 51, 53, 55, 61–63, 65, 71, 72, 74, 75, 81, 83, 84, 86, 92, 93, 95, 102, 108, 110, 112, 113, 117, 118, 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.…”

Section: Vehicle Detection and Trackingmentioning

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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“…Reference [19] also presented a method that works along the same lines. Reference [20] presented a method that uses color calibration to detect cars and then uses the angle of a fixed camera to translate pixel velocities to real world velocities. Camera properties are can also be used for transformation based prediction, as in [14].…”

Section: Related Workmentioning

confidence: 99%

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

2021

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Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

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Single camera vehicles speed measurement

Cited by 9 publications

References 4 publications

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

Vision‐based vehicle speed estimation: A survey

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

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