Vehicle Distance and Speed Estimation Algorithm for Computer Vision Sensor System

Gorobetz, Mikhail; Timofejevs, Jurijs; Srinivasa, Ambuja Bangalore

doi:10.1109/rtucon53541.2021.9711733

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Before the development of the software could begin, it was necessary to solve the trigonometric task of detecting the distance to an object in the field of view of the camera. This was one of the first tasks and was described in detail in [31]. Here, we provide a shortened explanation of the principle.…”

Section: Software and Distance Measuring Algorithm Descriptionmentioning

confidence: 99%

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

Gorobetz,

Timofejevs,

Potapovs

et al. 2024

Electronics

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This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device’s ability to function under various adverse conditions. The results demonstrate the system’s capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments.

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Section: Software and Distance Measuring Algorithm Descriptionmentioning

confidence: 99%

IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks

Gorobetz,

Timofejevs,

Potapovs

et al. 2024

Electronics

Self Cite

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“…[25] proposes a similar method which uses Tiny YOLO and estimating neighbouring vehicle speed by tracking their taillights. [26] presents an approach to this problem for indoor environments by using a 2D LiDAR and a low cost camera.…”

Section: B Environment Modellingmentioning

confidence: 99%

An Overview of different Autonomous Vehicle Algorithms for Sensing and Route Planning

Sudarshan,

2023

Recent Trends in Data Science and Its Applications

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The topic of self-driving cars has recently gained popularity. This paper presents an overview of several algorithms often employed in autonomous vehicle sensing and path planning. We have compiled a list of different algorithms used for these two purposes and provided a description of the same. A brief description of different sensors typically used in autonomous vehicles has also been provided. Sensor algorithms have been divided into 'Locational and Positioning' and 'Environmental modeling'. Location and Positioning Algorithms require the use of GPS, IMU, and Encoders to estimate accurate distance and use some variant of the Kalman Filter. Environmental modeling algorithms mainly rely on Cameras and use deep-learning object detection models like YOLO. There are also methods that require the use of Cameras with LiDAR and RADAR. For path planning, a description of the two most popular methods A* and Dijkstra has been provided along with their comparison.

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