EyeBus: Bus Detection and Route Alerts Using Computer Vision

This computer vision based automated bus announcement system improves public transportation by identifying buses and recognizing the route information on them from live camera feeds. This technology uses video data for real-time bus identification and route identification. The system receives the live camera feeds via advanced computer vision algorithms to identify buses and their routes in all kinds of weather conditions. It automatically provides audio and visual information at the bus stop for updating accordingly to the passengers with disabilities. It uses artificial intelligence (AI) and machine learning (ML) in the system adapts to dynamic changes such as schedule variations or route modifications, offering improved reliability and responsiveness. This work emphasizes accessibility, operational efficiency, and passenger convenience. Future developments aim to scale the system for broader deployment, refine recognition accuracy, and incorporate additional features to enhance usability and effectiveness in public transportation.

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Publication Details

Published
2025-07-04
DOI
https://doi.org/10.1109/icctdc64446.2025.11159005
Primary Topic
Video Surveillance and Tracking Methods
Type
article
Field-Weighted Citation Impact
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article

EyeBus: Bus Detection and Route Alerts Using Computer Vision

Alan Thomas, Alfred Antony, Gripsy Paul, Anson Saju George et al.
Video Surveillance and Tracking Methods
article

EyeBus: Bus Detection and Route Alerts Using Computer Vision

Alan Thomas, Alfred Antony, Gripsy Paul, Anson Saju George, Divya KS, Fabiya Philomina M J
article en

Abstract

This computer vision based automated bus announcement system improves public transportation by identifying buses and recognizing the route information on them from live camera feeds. This technology uses video data for real-time bus identification and route identification. The system receives the live camera feeds via advanced computer vision algorithms to identify buses and their routes in all kinds of weather conditions. It automatically provides audio and visual information at the bus stop for updating accordingly to the passengers with disabilities. It uses artificial intelligence (AI) and machine learning (ML) in the system adapts to dynamic changes such as schedule variations or route modifications, offering improved reliability and responsiveness. This work emphasizes accessibility, operational efficiency, and passenger convenience. Future developments aim to scale the system for broader deployment, refine recognition accuracy, and incorporate additional features to enhance usability and effectiveness in public transportation.

Institute of Engineering (NP)
Openalex Percentile: Top 75%
Video Surveillance and Tracking Methods
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EyeBus: Bus Detection and Route Alerts Using Computer Vision — Alan Thomas, Alfred Antony, et al. · (2025) | TGRS Research Map | TGRS