An Efficient Method for Addressing COVID-19 Proximity Related Issues in Autonomous Shuttles Public Transportation

Tsiktsiris, Dimitris; Lalas, Antonios; Dasygenis, Minas; Votis, Konstantinos; Tzovaras, Dimitrios

doi:10.1007/978-3-031-08333-4_14

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Very recently, a single overhead fisheye camera was proposed to detect social distance violations in buses (which is coarser goal than distance estimation), but no quantitative results were published [19]. Fisheye-stereo is often used in front-facing configuration for distance estimation in autonomous navigation [14,20], but recently it was proposed in overhead configuration for person re-identification indoors based on location rather than appearance [3].…”

Section: Related Workmentioning

confidence: 99%

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

Cokbas

Ishwar

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics. A natural resource to accomplish this are surveillance cameras. Unlike previous distance estimation methods, we use a single, overhead, fisheye camera with wide area coverage and propose two approaches. One method leverages a geometric model of the fisheye lens, whereas the other method uses a neural network to predict the 3D-world distance from people-locations in a fisheye image. To evaluate our algorithms, we collected a first-of-its-kind dataset using single fisheye camera, that comprises a wide range of distances between people (1-58 ft) and will be made publicly available. The algorithms achieve 1-2 ft distance error and over 95% accuracy in detecting social-distance violations.

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Section: Related Workmentioning

confidence: 99%

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

Cokbas

Ishwar

et al. 2023

Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…In [16], a method was suggested to see things using a special computer program. In [17], pictures and smart computer methods were used to address CoViD-19 proximity issues. In [18], a method was introduced to improve signature verification, achieving an accuracy of 85-95%.…”

Section: Wwwetasrcom Sajini and Pushpa: Sensor Enabled Proximity Dete...mentioning

confidence: 99%

Sensor Enabled Proximity Detection with Hybridisation of IoT and Computer Vision Models to Assist the Visually Impaired

Sajini,

Pushpa

2023

Eng. Technol. Appl. Sci. Res.

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Proximity Detection Systems (PDS) are used to detect objects or persons close to Visually Impaired (VI) persons. Sensors are used to identify proximity based on the distance from objects. This study aimed to design a hybrid proximity detection framework for VI people using ultrasonic sensors embedded in a Raspberry Pi board to detect the proximity of a VI user in an office environment. Hybridization was based on the integration of IoT-enabled devices, ultrasonic proximity sensors, and computer vision algorithms to control the detection of objects or people and inform the user with a voice message. The model framework was implemented with 100 samples and tested with 10 analyses in each sample. The results showed significant improvement in detecting the proximity of the objects with an accuracy of 98.7%, outperforming current PDS with good results in precision, range, obstacle recognition, false positives and negatives, response time, usability, durability, reliability, etc.

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In-Vehicle Services to Improve the User Experience and Security when Traveling with Automated Minibuses

Tsiktsiris,

Vafeiadis,

Lalas

et al. 2024

Contributions to Management Science

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This chapter looks at the innovative development and deployment of in-vehicle services to improve both the user experience and safety in automated minibuses, a critical component of future mobility-as-a-service (MaaS) ecosystems. With the rise of driverless public transport, ensuring the safety and comfort of passengers becomes a primary concern. The chapter discusses the integration of cutting-edge artificial intelligence (AI) and deep learning technologies to address these challenges, focusing on services such as enhanced safety and confidence, automated passenger presence monitoring, and intelligent feedback systems. It outlines the AVENUE project’s initiatives to create an AI-powered framework that not only promotes the widespread adoption of automated minibuses but also addresses social and personal safety concerns in the absence of a human driver. The services discussed aim to replace the driver’s monitoring and interaction functions with technology-enabled solutions, thereby maintaining service quality and promoting passenger confidence. Using real-world deployments and pilot studies, the chapter illustrates how these technological interventions contribute to the overall efficiency, safety, and acceptance of automated vehicles in urban environments and provide valuable insights into the future of automated public transport systems.

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An Efficient Method for Addressing COVID-19 Proximity Related Issues in Autonomous Shuttles Public Transportation

Cited by 4 publications

References 20 publications

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

Estimating Distances Between People Using a Single Overhead Fisheye Camera with Application to Social-Distancing Oversight

Sensor Enabled Proximity Detection with Hybridisation of IoT and Computer Vision Models to Assist the Visually Impaired

In-Vehicle Services to Improve the User Experience and Security when Traveling with Automated Minibuses

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