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

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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References

  1. 1.

    Indian Railways Statistical Publications 2016–17: Statistical summary—Indian Railways (PDF) Ministry of Railway. Archived (PDF) from the original on 22 February 2018. Retrieved 22 Feb 2018

  2. 2.

    Choudhury A (2010) Human-elephant conflicts in northeast India . Hum Dimens Wildl 9(4):261–270. https://doi.org/10.1080/10871200490505693

    Article  Google Scholar 

  3. 3.

    Dasgupta S, Ghosh AK (2015) Elephant–railway conflict in a biodiversity hotspot: determinants and perceptions of the conflict in Northern West Bengal, India. Hum Dimens Wildl 20:81–94. https://doi.org/10.1080/10871209.2014.937017

    Article  Google Scholar 

  4. 4.

    Roy M, Sukumar R (2017) Railways and wildlife: a case study of train-elephant collisions in Northern West Bengal, India. In: Borda-de-Água L, Barrientos R, Beja P, Pereira H (eds) Railway ecology. Springer, Cham, pp 157–177

    Google Scholar 

  5. 5.

    Zeppelzauer M, Stoeger A, Breiteneder C (2013) Acoustic detection of elephant presence in noisy environments. In: MAED 2013—Proceedings of the 2nd ACM international workshop on multimedia analysis for ecological data, pp 3–8

  6. 6.

    Saritha B, Elakiya P, Mathavi S, Monika M, Nivetha V (2017) To Prevent the animals accident and trackcrack detection system for railways. Int J Innov Res Comput CommunEng 5(3):4752–4758

    Google Scholar 

  7. 7.

    Punitha A, Nivetha A, Monisha J, Sagadevan K (2018) Detection and emergency response system for preventing human elephant conflict using vibration sensor. Int J Pure Appl Math 119(14):1033–1037

    Google Scholar 

  8. 8.

    Sugumar SJ, Jayaparvathy R (2014) An improved real-time image detection system for elephant intrusion along the forest border areas. Sci World J. https://doi.org/10.1155/2014/393958

    Article  Google Scholar 

  9. 9.

    Dabarera R, Rodrigo R (2010) Vision based elephant recognition for management and conservation. In: Fifth international conference on information and automation for sustainability, pp 163–166

  10. 10.

    Dabarera R, Rodrigo R (2010) Vision based elephant recognition for management and conservation. In: ICIAfS10, pp 163–166

  11. 11.

    Shukla P, Dua I, Raman B, Mittal A (2017) A computer vision framework for detecting and preventing Human-Elephant Collisions, ICCV, pp 2883–2890.

  12. 12.

    Kushwaha SPS, Roy PS (2002) Geospatial technology for wildlife habitat evaluation. Int Soc Trop Ecol 43(1):137–150

    Google Scholar 

  13. 13.

    Zeppelzauer M (2013) Automated detection of elephants in wildlife video. EURASIP J Image Video Process 46:1–23

    Google Scholar 

  14. 14.

    Dua I, Shukla P, Mittal A (2015) A vision based human–elephant collision detection system. In: 3rd Int. conf. on image information processing, pp 255–229

  15. 15.

    Real-time Animal Detection System for Intelligent Vehicles (2014) (PDF), University of Ottawa, Archived (PDF) from the original on 24 February 2018. Retrieved 24 Feb 2018

  16. 16.

    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput SocConf Comput Vis Pattern Recogn (CVPR) 1:886–893

    Google Scholar 

  17. 17.

    Pietikäinen M (2010) Local binary patterns. Scholarpedia 5(3):9775

    Article  Google Scholar 

  18. 18.

    Daxini N, Sharma S, Patel R (2015) Real time animal detection system using HAAR like feature. Int J Innov Res Comput CommunEng 3(6):5177–5182

    Article  Google Scholar 

  19. 19.

    Burghardt T, Calic J (2006) Real-time face detection and tracking of animals. In: 2006 8th seminar on neural network applications in electrical engineering, pp 27–32

  20. 20.

    Sharma SU, Shah DJ (2017) A practical animal detection and collision avoidance system using computer vision technique. IEEE Access 5:347–358

    Article  Google Scholar 

  21. 21.

    Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR, pp 511–518

  22. 22.

    Wang R (2012) AdaBoost for feature selection, classification and its relation with SVM, a review. Int Conf Solid State Dev Mater Sci 25:800–807

    Google Scholar 

  23. 23.

    Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    MathSciNet  Article  Google Scholar 

  24. 24.

    Rong W, Li Z, Zhang W, Sun L (2014) An improved Canny edge detection algorithm. In: 2014 IEEE international conference on mechatronics and automation, pp 577–582

  25. 25.

    Ojala T, Pietikäinen M, Harwood D (1996) Pattern Recogn 29(1):51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  26. 26.

    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  27. 27.

    Cruz JEC, Shiguemori EH, Guimaraes LNF (2015) A comparison of Haar-like, LBP and HOG approaches to concrete and asphalt runway detection in high resolution imagery. J Comput Int Sci 6(3):121–136

    Google Scholar 

  28. 28.

    Adouani A, Henia WMB, Lachiri Z (2019) Comparison of Haar-like, HOG and LBP approaches for face detection in video sequences. In: 16th Int. multi-conf. on systems, signals & devices, pp 266–271

  29. 29.

    Rangdal MB, Hanchate DB (2014) Animal detection using histogram orinted gradient. Int J Recent Innov Trends Comput Commun 2(2):178–183

    Google Scholar 

  30. 30.

    Adiono T, Prakoso KS, Putratama CD, Yuwono B, Fuada S (2018) HOG-AdaBoost implementation for human detection employing FPGA ALTERA DE2-115. Int J Adv Comput Sci Appl 9(10):353–358

    Google Scholar 

  31. 31.

    Jian Wu, Cui Z, Sheng VS, Zhao P, Dongliang Su, Gong S (2013) Comparative study of SIFT and its variants. Meas Sci Rev 13(3):122–131

    Article  Google Scholar 

  32. 32.

    Gritti T, Shan C, Jeanne V, Braspenning R (2008) Local features based facial expression recognition with face registration errors. In: IEEE International conference on automatic face and gesture recognition, pp 1–8. https://doi.org/10.1109/AFGR.2008.4813379.

  33. 33.

    Lienhart R, Kuranov A, Pisarevsky V (2003) Empirical analysis of detection cascades of boosted classifiers for rapid object detection. Pattern Recogn. https://doi.org/10.1007/978-3-540-45243-039

    Article  Google Scholar 

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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