{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T16:21:54Z","timestamp":1787588514140,"version":"build-2736575974"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00500-021-06493-8","type":"journal-article","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T09:02:36Z","timestamp":1636707756000},"page":"4005-4018","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep vision-based surveillance system to prevent train\u2013elephant collisions"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-8369","authenticated-orcid":false,"given":"Surbhi","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj","family":"Mohan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Padmalaya","family":"Nayak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Chythanya","family":"Nagaraju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhavi","family":"Karanam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"issue":"6","key":"6493_CR1","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1016\/j.patcog.2007.11.010","volume":"41","author":"A Ardovini","year":"2008","unstructured":"Ardovini A, Cinque L, Sangineto E (2008) Identifying elephant photos by multi-curve matching. Pattern Recogn 41(6):1867\u20131877","journal-title":"Pattern Recogn"},{"key":"6493_CR2","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.ecoleng.2017.06.024","volume":"106","author":"JAJ Backs","year":"2017","unstructured":"Backs JAJ, Nychka JA, Clair CS (2017) Warning systems triggered by trains could reduce collisions with wildlife. Ecol Eng 106:563\u2013569","journal-title":"Ecol Eng"},{"key":"6493_CR3","doi-asserted-by":"crossref","unstructured":"Beery S, Van Horn G, Perona P (2018) Recognition in terra incognita. In: Proceedings of the European conference on computer vision (ECCV), pp 456\u2013473","DOI":"10.1007\/978-3-030-01270-0_28"},{"key":"6493_CR4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.jenvman.2019.02.076","volume":"237","author":"M B\u00edl","year":"2019","unstructured":"B\u00edl M, Andr\u00e1\u0161ik R, Du\u013ea M, Sedon\u00edk J (2019) On reliable identification of factors influencing wildlife-vehicle collisions along roads. J Environ Manage 237:297\u2013304","journal-title":"J Environ Manage"},{"issue":"3","key":"6493_CR5","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1049\/ip-vis:20050052","volume":"153","author":"T Burghardt","year":"2006","unstructured":"Burghardt T, \u0106ali\u0107 J (2006) Analysing animal behaviour in wildlife videos using face detection and tracking. IEE Proc vis Image Signal Process 153(3):305\u2013312","journal-title":"IEE Proc vis Image Signal Process"},{"key":"6493_CR6","doi-asserted-by":"crossref","unstructured":"Chen G, Han TX, He Z, Kays R, Forrester T (2014) Deep convolutional neural network-based species recognition for wild animal monitoring. In: 2014 IEEE international conference on image processing (ICIP). pp 858\u2013862. IEEE","DOI":"10.1109\/ICIP.2014.7025172"},{"key":"6493_CR7","unstructured":"Chythanya K, Madhavi K, Ramesh G (2020) A Machine learning enabled IoT device to combat elephant mortality on railway tracks. In: Springer Proceedings of 2nd international conference on innovative data communication technologies and applications (ICIDCA 2020)-Sept. 2020. In press"},{"key":"6493_CR8","doi-asserted-by":"crossref","unstructured":"Devost E, Lai S, Casajus N, Berteaux D (2019) Fox Mask: a new automated tool for animal detection in camera trap images. BioRxiv, p 640037","DOI":"10.1101\/640037"},{"key":"6493_CR9","doi-asserted-by":"crossref","unstructured":"Dhanaraj JSA, Kumar Sangaiah A (2018) Elephant detection using boundary sense deep learning (BSDL) architecture. J Exp Theor Artif Intell, pp 1\u201316","DOI":"10.1080\/0952813X.2018.1552316"},{"key":"6493_CR10","doi-asserted-by":"crossref","unstructured":"Farah R, Langlois JP, Bilodeau GA (2011) Rat: robust animal tracking. In: 2011 IEEE international symposium on robotic and sensors environments (ROSE), pp 65\u201370. IEEE","DOI":"10.1109\/ROSE.2011.6058509"},{"key":"6493_CR11","doi-asserted-by":"crossref","unstructured":"Gadekallu TR, Rajput DS, Reddy MPK, Lakshmanna K, Bhattacharya S, Singh S, Jolfaei A, Alazab M (2020) A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Process, pp 1\u201314","DOI":"10.1007\/s11554-020-00987-8"},{"key":"6493_CR12","doi-asserted-by":"crossref","unstructured":"Gadekallu TR, Alazab M, Kaluri R, Maddikunta PKR, Bhattacharya S, Lakshmanna K, Parimala M (2021) Hand gesture classification using a novel CNN-crow search algorithm. Complex Intell Syst, pp 1\u201314","DOI":"10.1007\/s40747-021-00324-x"},{"issue":"23","key":"6493_CR13","doi-asserted-by":"publisher","first-page":"34157","DOI":"10.1007\/s11042-019-08232-6","volume":"78","author":"S Gupta","year":"2019","unstructured":"Gupta S, Kumar M, Garg A (2019) Improved object recognition results using SIFT and ORB feature detector. Multimed Tools Appl 78(23):34157\u201334171","journal-title":"Multimed Tools Appl"},{"key":"6493_CR14","doi-asserted-by":"crossref","unstructured":"Hannuna SL, Campbell NW, Gibson DP (2005) Segmenting quadruped gait patterns from wildlife video","DOI":"10.1049\/cp:20050095"},{"key":"6493_CR15","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"6493_CR16","doi-asserted-by":"crossref","unstructured":"Iwendi C, Srivastava G, Khan S, Maddikunta PKR (2020) Cyberbullying detection solutions based on deep learning architectures. Multimed Syst, pp 1\u201314","DOI":"10.1007\/s00530-020-00701-5"},{"issue":"1","key":"6493_CR17","first-page":"434","volume":"7","author":"R Jayakumar","year":"2020","unstructured":"Jayakumar R, Swaminathan R, Harikumar S, Banupriya N, Saranya S (2020) Animal detection using deep learning algorithm. J Crit Rev 7(1):434\u2013439","journal-title":"J Crit Rev"},{"key":"6493_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.rse.2018.06.028","volume":"216","author":"B Kellenberger","year":"2018","unstructured":"Kellenberger B, Marcos D, Tuia D (2018a) Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning. Remote Sens Environ 216:139\u2013153","journal-title":"Remote Sens Environ"},{"issue":"12","key":"6493_CR19","doi-asserted-by":"publisher","first-page":"9524","DOI":"10.1109\/TGRS.2019.2927393","volume":"57","author":"B Kellenberger","year":"2019","unstructured":"Kellenberger B, Marcos D, Lobry S, Tuia D (2019) Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning. IEEE Trans Geosci Remote Sens 57(12):9524\u20139533","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6493_CR20","doi-asserted-by":"crossref","unstructured":"Kellenberger B, Volpi M, Tuia D (2017) Fast animal detection in UAV images using convolutional neural networks. In: 2017 IEEE international geoscience and remote sensing symposium (IGARSS) (pp 866\u2013869). IEEE","DOI":"10.1109\/IGARSS.2017.8127090"},{"key":"6493_CR21","doi-asserted-by":"crossref","unstructured":"Kellenberger B, Marcos D, Tuia D (2018) Best practices to train deep models on imbalanced datasets\u2014a case study on animal detection in aerial imagery. In: Joint European conference on machine learning and knowledge discovery in databases. pp 630\u2013634. Springer, Cham","DOI":"10.1007\/978-3-030-10997-4_40"},{"issue":"1","key":"6493_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.7763\/IJFCC.2012.V1.7","volume":"1","author":"BT Koik","year":"2012","unstructured":"Koik BT, Ibrahim H (2012) A literature survey on animal detection methods in digital images. Int J Future Comput Commun 1(1):24","journal-title":"Int J Future Comput Commun"},{"key":"6493_CR23","doi-asserted-by":"crossref","unstructured":"Korschens M, Denzler J (2019) Elpephants: a fine-grained dataset for elephant re-identification. In: Proceedings of the IEEE international conference on computer vision workshops, pp 0\u20130","DOI":"10.1109\/ICCVW.2019.00035"},{"key":"6493_CR24","doi-asserted-by":"publisher","first-page":"163912","DOI":"10.1109\/ACCESS.2019.2952176","volume":"7","author":"M Kumar","year":"2019","unstructured":"Kumar M, Gupta S, Gao XZ, Singh A (2019a) Plant species recognition using morphological features and adaptive boosting methodology. IEEE Access 7:163912\u2013163918","journal-title":"IEEE Access"},{"issue":"2","key":"6493_CR25","first-page":"104","volume":"8","author":"S Kumar","year":"2019","unstructured":"Kumar S, Baline HV, Sivakumar T, Potluri VP (2019b) detection of wild elephants using image processing on raspberry PI3. Int J Comput Sci Mobile Comput 8(2):104\u2013115","journal-title":"Int J Comput Sci Mobile Comput"},{"key":"6493_CR26","unstructured":"Langbein J (2011) Monitoring reported deer road casualties and related accidents in England to 2010. Research Report 2011\/3. The Deer Initiative, Wrexham, UK"},{"key":"6493_CR27","doi-asserted-by":"crossref","unstructured":"Mammeri A, Zhou D, Boukerche A, Almulla M (2014) An efficient animal detection system for smart cars using cascaded classifiers. In: 2014 IEEE international conference on communications (ICC), pp 1854\u20131859. IEEE","DOI":"10.1109\/ICC.2014.6883593"},{"key":"6493_CR28","first-page":"1","volume-title":"Advanced computational and communication paradigms","author":"RK Mandal","year":"2018","unstructured":"Mandal RK, Bhutia DD (2018) A proposed artificial neural network (ANN) model using geophone sensors to detect elephants near the railway tracks. Advanced computational and communication paradigms. Springer, Singapore, pp 1\u20136"},{"key":"6493_CR29","unstructured":"Marais JC (2018) Automated elephant detection and classification from aerial infrared and colour images using deep learning (Doctoral dissertation, Stellenbosch: Stellenbosch University)"},{"key":"6493_CR30","unstructured":"M\u00f6nck HJ, J\u00f6rg A, von Falkenhausen T, Tanke J, Wild B, Dormagen D, Piotrowski J, Winklmayr C, Bierbach D, Landgraf T (2018) BioTracker: an open-source computer vision framework for visual animal tracking. arXiv preprint arXiv:1803.07985"},{"key":"6493_CR31","unstructured":"Morse G, Liu T, Gilchrist A, Halliday N, Heavisides J, Hopper D, McKay S, Nowell R, Pitman S, Woods M (2014) Analysis of the risk from animals on the line \u2013 Issue 2. Report. Rail Safety and Standards Board, London, UK"},{"key":"6493_CR32","unstructured":"Naude J, Joubert D (2019). The aerial elephant dataset: a new public benchmark for aerial object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 48\u201355"},{"issue":"25","key":"6493_CR33","doi-asserted-by":"publisher","first-page":"E5716","DOI":"10.1073\/pnas.1719367115","volume":"115","author":"MS Norouzzadeh","year":"2018","unstructured":"Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Palmer MS, Packer C, Clune J (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc Natl Acad Sci 115(25):E5716\u2013E5725","journal-title":"Proc Natl Acad Sci"},{"key":"6493_CR34","doi-asserted-by":"crossref","unstructured":"Patman J, Michael SC, Lutnesky MM, Palaniappan K (2018) Biosense: real-time object tracking for animal movement and behavior research. In 2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp 1\u20138. IEEE","DOI":"10.1109\/AIPR.2018.8707411"},{"issue":"2","key":"6493_CR35","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1007\/s00500-019-03969-6","volume":"24","author":"T Praczyk","year":"2020","unstructured":"Praczyk T (2020) Neural collision avoidance system for biomimetic autonomous underwater vehicle. Soft Comput 24(2):1315\u20131333","journal-title":"Soft Comput"},{"key":"6493_CR36","unstructured":"Raja MAA, Ramya MK, Kousalya MB, Jeeva MS (2018) Prevention of wild animals from accidents using image detection and edge algorithm. PREVENTION, 5(11)"},{"key":"6493_CR37","doi-asserted-by":"crossref","unstructured":"Ramanan D, Forsyth DA (2003) Using temporal coherence to build models of animals. p 338. IEEE","DOI":"10.1109\/ICCV.2003.1238364"},{"issue":"5","key":"6493_CR38","doi-asserted-by":"publisher","first-page":"6291","DOI":"10.3233\/JIFS-179710","volume":"38","author":"S Ravikumar","year":"2020","unstructured":"Ravikumar S, Vinod D, Ramesh G, Pulari SR, Mathi S (2020) A layered approach to detect elephants in live surveillance video streams using convolution neural networks. J Intell Fuzzy Syst 38(5):6291\u20136298","journal-title":"J Intell Fuzzy Syst"},{"key":"6493_CR39","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"6493_CR40","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91\u201399","journal-title":"Adv Neural Inf Process Syst"},{"key":"6493_CR41","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.rse.2017.08.026","volume":"200","author":"N Rey","year":"2017","unstructured":"Rey N, Volpi M, Joost S, Tuia D (2017) Detecting animals in African Savanna with UAVs and the crowds. Remote Sens Environ 200:341\u2013351","journal-title":"Remote Sens Environ"},{"issue":"4","key":"6493_CR42","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1111\/2041-210X.13133","volume":"10","author":"S Schneider","year":"2019","unstructured":"Schneider S, Taylor GW, Linquist S, Kremer SC (2019) Past, present and future approaches using computer vision for animal re-identification from camera trap data. Methods Ecol Evol 10(4):461\u2013470","journal-title":"Methods Ecol Evol"},{"issue":"9","key":"6493_CR43","doi-asserted-by":"publisher","first-page":"6687","DOI":"10.1007\/s00500-019-04306-7","volume":"24","author":"AN Sharkawy","year":"2020","unstructured":"Sharkawy AN, Koustoumpardis PN, Aspragathos N (2020) Human\u2013robot collisions detection for safe human\u2013robot interaction using one multi-input\u2013output neural network. Soft Comput 24(9):6687\u20136719","journal-title":"Soft Comput"},{"key":"6493_CR44","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1109\/ACCESS.2016.2642981","volume":"5","author":"SU Sharma","year":"2016","unstructured":"Sharma SU, Shah DJ (2016) A practical animal detection and collision avoidance system using computer vision technique. IEEE Access 5:347\u2013358","journal-title":"IEEE Access"},{"key":"6493_CR45","unstructured":"Shukla P, Dua I, Raman B, Mittal A (2017) A computer vision framework for detecting and preventing human-elephant collisions. In: Proceedings of the IEEE international conference on computer vision workshops, pp 2883\u20132890"},{"key":"6493_CR46","doi-asserted-by":"crossref","unstructured":"Sugumar SJ, Jayaparvathy R (2014) An improved real time image detection system for elephant intrusion along the forest border areas. Sci World J","DOI":"10.1155\/2014\/393958"},{"key":"6493_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"6","key":"6493_CR48","first-page":"33","volume":"3","author":"M Tanwar","year":"2017","unstructured":"Tanwar M, Shekhawat NS, Panwar S (2017) A survey on algorithms on animal detection. Int J Future Revolut Comput Sci Commun Engineering. 3(6):33\u201335","journal-title":"Int J Future Revolut Comput Sci Commun Engineering."},{"key":"6493_CR49","doi-asserted-by":"crossref","unstructured":"Tweed D, Calway A (2002) Tracking multiple animals in wildlife footage. In: Object recognition supported by user interaction for service robots, vol 2, pp 24\u201327, IEEE","DOI":"10.1109\/ICPR.2002.1048227"},{"key":"6493_CR50","unstructured":"Venkataraman AB, Saandeep R, Baskaran N, Roy M, Madhivanan A, Sukumar R (2005) Using satellite telemetry to mitigate Elephant\u2013human conflict: an experiment in northern West Bengal, India. Current Science, pp 1827\u20131831"},{"issue":"2","key":"6493_CR51","doi-asserted-by":"publisher","first-page":"e54700","DOI":"10.1371\/journal.pone.0054700","volume":"8","author":"C Vermeulen","year":"2013","unstructured":"Vermeulen C, Lejeune P, Lisein J, Sawadogo P, Bouch\u00e9 P (2013) Unmanned aerial survey of elephants. PLoS ONE 8(2):e54700","journal-title":"PLoS ONE"},{"key":"6493_CR52","doi-asserted-by":"crossref","unstructured":"Zendel O, Murschitz M, Zeilinger M, Steininger D, Abbasi S, Beleznai C (2019) Railsem19: a dataset for semantic rail scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 0\u20130","DOI":"10.1109\/CVPRW.2019.00161"},{"issue":"1","key":"6493_CR53","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/1687-5281-2013-46","volume":"2013","author":"M Zeppelzauer","year":"2013","unstructured":"Zeppelzauer M (2013) Automated detection of elephants in wildlife video. EURASIP J Image Video Process 2013(1):46","journal-title":"EURASIP J Image Video Process"},{"issue":"1","key":"6493_CR54","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1186\/s13104-015-1370-y","volume":"8","author":"M Zeppelzauer","year":"2015","unstructured":"Zeppelzauer M, Stoeger AS (2015) Establishing the fundamentals for an elephant early warning and monitoring system. BMC Res Notes 8(1):409","journal-title":"BMC Res Notes"},{"key":"6493_CR55","doi-asserted-by":"crossref","unstructured":"Zotin AG, Proskurin AV (2019) Animal detection using a series of images under complex shooting conditions. Int Arch Photogramm Remote Sens Spat Inf Sci","DOI":"10.5194\/isprs-archives-XLII-2-W12-249-2019"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06493-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06493-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06493-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T21:18:25Z","timestamp":1726089505000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06493-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6493"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06493-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-192950\/v1","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,12]]},"assertion":[{"value":"23 October 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}