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Journal of Reliable Intelligent Environments
Erschienen in: Journal of Reliable Intelligent Environments 4/2021

22.01.2021 | Original Article

Elephant–railway conflict minimisation using real-time video data and machine learning

verfasst von: Sayan Dutta, Arati Paul, Debasish Chakraborty, G. Srinivasa Rao

Erschienen in: Journal of Reliable Intelligent Environments | Ausgabe 4/2021

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Abstract

Elephant–train collision has been a major issue for both the railway as well as the forest departments. In this study real-time video data is analysed for detecting elephant to alert the train driver in case of elephants crossing the railway track in which the train is approaching. The HAAR feature extraction and adaptive boosting-based machine learning algorithm are used for detecting elephants from real-time video data. The experimental result shows the average precision of the proposed technique in detecting elephants using real-time video data is more than 96%.
Vorheriger Artikel Internet of things based intelligent accident avoidance system for adverse weather and road conditions
Nächster Artikel CAFOR: congestion avoidance using fuzzy logic to find an optimal routing path in 6LoWPAN networks

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Metadaten
Titel
Elephant–railway conflict minimisation using real-time video data and machine learning
verfasst von
Sayan Dutta
Arati Paul
Debasish Chakraborty
G. Srinivasa Rao
Publikationsdatum
22.01.2021
Verlag
Springer International Publishing
Erschienen in
Journal of Reliable Intelligent Environments / Ausgabe 4/2021
Print ISSN: 2199-4668
Elektronische ISSN: 2199-4676
DOI
https://doi.org/10.1007/s40860-021-00131-8

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