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%.
Similar content being viewed by others
References
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
Choudhury A (2010) Human-elephant conflicts in northeast India . Hum Dimens Wildl 9(4):261–270. https://doi.org/10.1080/10871200490505693
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
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
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
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
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
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
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
Dabarera R, Rodrigo R (2010) Vision based elephant recognition for management and conservation. In: ICIAfS10, pp 163–166
Shukla P, Dua I, Raman B, Mittal A (2017) A computer vision framework for detecting and preventing Human-Elephant Collisions, ICCV, pp 2883–2890.
Kushwaha SPS, Roy PS (2002) Geospatial technology for wildlife habitat evaluation. Int Soc Trop Ecol 43(1):137–150
Zeppelzauer M (2013) Automated detection of elephants in wildlife video. EURASIP J Image Video Process 46:1–23
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
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
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput SocConf Comput Vis Pattern Recogn (CVPR) 1:886–893
Pietikäinen M (2010) Local binary patterns. Scholarpedia 5(3):9775
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
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
Sharma SU, Shah DJ (2017) A practical animal detection and collision avoidance system using computer vision technique. IEEE Access 5:347–358
Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR, pp 511–518
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
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
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
Ojala T, Pietikäinen M, Harwood D (1996) Pattern Recogn 29(1):51–59. https://doi.org/10.1016/0031-3203(95)00067-4
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
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
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
Rangdal MB, Hanchate DB (2014) Animal detection using histogram orinted gradient. Int J Recent Innov Trends Comput Commun 2(2):178–183
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
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
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.
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
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors have no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dutta, S., Paul, A., Chakraborty, D. et al. Elephant–railway conflict minimisation using real-time video data and machine learning. J Reliable Intell Environ 7, 315–324 (2021). https://doi.org/10.1007/s40860-021-00131-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40860-021-00131-8