Detection of Vehicle Position and Speed using Camera Calibration and Image Projection Methods

Gunawan, Alexander Agung Santoso; Tanjung, Deasy Aprilia; Gunawan, Fergyanto E.

doi:10.1016/j.procs.2019.08.165

Cited by 20 publications

(23 citation statements)

References 6 publications

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“…With the exception of drone-based systems, most approaches are based on static cameras, making the vehicle detection task much easier thanks to the presence of a static background. 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 [38,39,57,99,110,111,116], as well as simple methods that rely on edges [49,87,95], gray-level features [45,117,123], binary features or patterns [71,87,96], corners [79], SIFT/SURF features [62,112] or KLT features [58,59,69,91].…”

Section: Static Backgroundmentioning

confidence: 99%

“…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%

“…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].5.1.2Feature-based Some approaches are based on the detection of different type of features grouped in regions within the vehicle area, for example, after background subtraction…”

mentioning

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: Static Backgroundmentioning

confidence: 99%

Section: Distance Estimationmentioning

confidence: 99%

Section: Speed Estimationmentioning

confidence: 99%

mentioning

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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“…But it cannot be applied for multi-vehicle detection. And vehicle detection is not influenced by the vehicle shadow [4].…”

Section: Related Workmentioning

confidence: 99%

Development of Vehicle Speed Estimation Technique using Image Processing

Raikar¹,

Arakeri²

2020

IJERT

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In India, the surveillance system and intelligent traffic management is the essential requirement for smart city improvement. But nowadays, the number of accidents on the road rises every year due to more vehicles on the road. Vehicle speed is only one reason for road accidents. So that traffic management authority needs a better surveillance system. Due to the significant expense of radar and less precision, the radar system is not able to become popular in the traffic surveillance system. And the inductive loops technique also needs high support. Therefore it is necessary to propose some mechanisms to overcome such problems. This paper proposed a novel computer vision-based automated system for multi-vehicle detection, tracking, and estimation of vehicle speed in a video sequence by using image processing. This proposed method contains several steps for vehicle speed estimation. In the first step, pre-processing is applied to extracted frames to reduce noise. And then, the gradient edge detection algorithm is used for moving vehicle edge extraction. The next step is template matching by using a normalized cross-correlation method. In this proposed system, detected vehicles tracked by using the vehicle's frame coordinates. Finally, speed is estimated using the number of frames and frame rate. The detection and tracking accuracy achieved by the proposed approach is about 97.33% and 97.2%. Overall precision, error rate, F1_score, sensitivity, kappa value is 98.19%, 0.0267, 97.43, 96.68% and 0.9466 respectively.

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A Practical Approach for Vehicle Speed Estimation in Smart Cities

Barra¹,

Carta²,

Meloni³

et al. 2023

Lecture Notes in Computer Science

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The last few decades have witnessed the increasing deployment of digital technologies in the urban environment with the goal of creating improved services to citizens especially related to their safety. This motivation, enabled by the widespread evolution of cutting edge technologies within the Artificial Intelligence, Internet of Things, and Computer Vision, has led to the creation of smart cities. One example of services that different cities are trying to provide to their citizens is represented by evolved video surveillance systems that are able to identify perpetrators of unlawful acts of vandalism against public property, or any other kind of illegal behaviour. Following this direction, in this paper, we present an approach that exploits existing video surveillance systems to detect and estimate vehicle speed. The system is currently being used by a municipality of Sardinia, an Italian region. An existing system leveraging Convolutional Neural Networks has been employed to tackle object detection and tracking tasks. An extensive experimental evaluation has been carried out on the Brno dataset and against stateof-the-art competitors showing excellent results of our approach in terms of flexibility and speed detection accuracy.

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Detection of Vehicle Position and Speed using Camera Calibration and Image Projection Methods

Cited by 20 publications

References 6 publications

Vision‐based vehicle speed estimation: A survey

Vision‐based vehicle speed estimation: A survey

Development of Vehicle Speed Estimation Technique using Image Processing

A Practical Approach for Vehicle Speed Estimation in Smart Cities

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