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

Journal of Reliable Intelligent Environments (2021)Cite this article

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%.

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Author information

Affiliations

  1. Institute of Engineering and Management, Kolkata, India

    Sayan Dutta

  2. Regional Remote Sensing Centre-East, NRSC, ISRO, Kolkata, India

    Arati Paul & Debasish Chakraborty

  3. ISRO Headquarters, ISRO, Bangalore, India

    G. Srinivasa Rao

Authors
  1. Sayan Dutta
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  2. Arati Paul
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  3. Debasish Chakraborty
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  4. G. Srinivasa Rao
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Correspondence to Arati Paul.

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Dutta, S., Paul, A., Chakraborty, D. et al. Elephant–railway conflict minimisation using real-time video data and machine learning. J Reliable Intell Environ (2021). https://doi.org/10.1007/s40860-021-00131-8

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Keywords

  • Elephant detection
  • Computer vision
  • Haar feature
  • Cascade classifier
  • Video analytics
  • Image processing