Establishing the fundamentals for an elephant early warning and monitoring system

BMC Research Notes

Angela S. Stoeger²  Matthias Zeppelzauer¹ 

[1] Media Computing Group, St. Pölten University of Applied Sciences, Matthias-Corvinus Strasse 15, St. Pölten, 3100, Austria;Department of Cognitive Biology, University of Vienna, Althanstrasse 14, Vienna, 1090, Austria

关键词: Human–elephant conflict;    Visual tracking;    Visual monitoring;    Call classification;    Automatic call detection;    Acoustic monitoring;    Vocalizations;    Loxodonta africana;    Elephants;   

Others  :  1230375
DOI  :  10.1186/s13104-015-1370-y

 received in 2015-03-05, accepted in 2015-08-18,  发布年份 2015

【 摘 要 】

Background

The decline of habitat for elephants due to expanding human activity is a serious conservation problem. This has continuously escalated the human–elephant conflict in Africa and Asia. Elephants make extensive use of powerful infrasonic calls (rumbles) that travel distances of up to several kilometers. This makes elephants well-suited for acoustic monitoring because it enables detecting elephants even if they are out of sight. In sight, their distinct visual appearance makes them a good candidate for visual monitoring. We provide an integrated overview of our interdisciplinary project that established the scientific fundamentals for a future early warning and monitoring system for humans who regularly experience serious conflict with elephants. We first draw the big picture of an early warning and monitoring system, then review the developed solutions for automatic acoustic and visual detection, discuss specific challenges and present open future work necessary to build a robust and reliable early warning and monitoring system that is able to operate in situ.

Findings

We present a method for the automated detection of elephant rumbles that is robust to the diverse noise sources present in situ. We evaluated the method on an extensive set of audio data recorded under natural field conditions. Results show that the proposed method outperforms existing approaches and accurately detects elephant rumbles. Our visual detection method shows that tracking elephants in wildlife videos (of different sizes and postures) is feasible and particularly robust at near distances.

Discussion

From our project results we draw a number of conclusions that are discussed and summarized. We clearly identified the most critical challenges and necessary improvements of the proposed detection methods and conclude that our findings have the potential to form the basis for a future automated early warning system for elephants. We discuss challenges that need to be solved and summarize open topics in the context of a future early warning and monitoring system. We conclude that a long-term evaluation of the presented methods in situ using real-time prototypes is the most important next step to transfer the developed methods into practical implementation.

【 授权许可】

   
2015 Zeppelzauer and Stoeger.

【 图 表 】

Fig.1.

Fig.2.

Fig.3.

Fig.4.

Fig.5.

Fig.6.

【 参考文献 】

• [1]Van Aarde RJ, Ferreira S, Jackson T, Page B, De Beer Y, et al.: Elephant population biology and ecology. In Elephant management: a scientific assessment for South Africa. Edited by Scholes RJ, Mennell KG. Society for Conservation Biology, Washington DC; 2008:84-145.
• [2]Santiapillai C, Wijeyamohan S, Bandara G, Athurupana R, Dissanayake N, et al.: An assessment of the human–elephant conflict in Sri Lanka. Ceylon J Sci Biol Sci 2010, 39:21-33.
• [3]Douglas-Hamilton I: The current elephant poaching trend. Pachyderm 2008, 45:154-157.
• [4]Lemieux AM, Clarke RV: The international ban on ivory sales and its effects on elephant poaching in Africa. Br J Criminol 2009, 49:451-471.
• [5]Hoare RE, Du Toit JD: Coexistence between people and elephants in African Savannas. Conserv Biol 1999, 13:633-639.
• [6]Pinter-Wolman N: Human–elephant conflict in Africa: the legal and political viability of translocations, wildlife corridors, and transfrontier parks for large mammal conservation. J Int Wildl Law Policy 2012, 15:152-166.
• [7]Douglas-Hamilton I, Krink T, Vollrath F: Movements and corridors of African elephants in relation to protected areas. Naturwissenschaften 2005, 92:158-163.
• [8]Renewable Energy Applications & Products. http://www.reap-india.com/elephant-early-warning-system.html. Accessed 14 Jul 2015.
• [9]Sugumar SJ, Jayaparvathy R: An early warning system for elephant intrusion along the forest border areas. Curr Sci 2013, 104:1515-1526.
• [10]Wrege PH, Rowland ED, Thompson BG, Batruch N: Use of acoustic tools to reveal otherwise cryptic responses of forest elephants to oil exploration. Conserv Biol 2010, 24:1578-1585.
• [11]Blumstein DT, Mennill DJ, Clemins P, Girod L, Yao K, et al.: Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus. J Appl Ecol 2011, 48:758-767.
• [12]Langbauer RW Jr: Elephant communication. Zoo Biol. 2000, 19:425-445.
• [13]Soltis J: Vocal communication in African elephants (Loxodonta africana). Zoo Biol. 2010, 29:192-209.
• [14]Nair BR, Balakrishnan R, Seelamantula CS, Sukumar R: Vocalizations of wild Asian elephants (Elephas maximus): structural classification and social context. J Acoust Soc Am 2009, 126:2768-2778.
• [15]Stoeger AS, de Silva S. African and Asian elephant vocal communication: a cross-species comparison. In: Witzany G, editor, Biocommunication of animals. 2014. doi:10.1007/978-94-007-7414-8_3.
• [16]Herbst CT, Stoeger AS, Frey R, Lohscheller J, Titze IR, Gumpenberger M, et al.: How low can you go—physical production mechanism of elephant infrasonic vocalization. Science 2012, 337:595-599.
• [17]Titze IR: Principles of voice production. Prentice-Hall, Englewood Cliffs; 1994.
• [18]Garstang M: Long-distance, low-frequency elephant communication. J Comp Physiol 2004, 190:791-805.
• [19]Payne KB, Thompsen M, Kramer L: Elephant calling patterns as indicators of group size and composition: the basis for an acoustic monitoring system. Afr J Ecol 2003, 41:99-107.
• [20]Seneviratne L, Rossel G, Gunasekera HLPA, Madanayake YMSS, Doluweera G. Elephant infrasound calls as a method for electronic elephant detection. In: Jayewardene J, editor. The Proceedings of the Symposium on Human–Elephant Relationships and Conflicts. Colombo; 2004, pp. 1–7.
• [21]Thompson ME, Schwager SJ, Payne KB: Heard but not seen: an acoustic survey of the African forest elephant population at Kakum Conservation Area, Ghana. Afr J Ecol 2009, 48:224-231.
• [22]Thompson ME, Schwager SJ, Payne KB, Turkalo AK: Acoustic estimation of wildlife abundance: methodology for vocal mammals in forested habitats. Afr J Ecol 2009, 48:654-661.
• [23]Stoeger A, Heimann G, Zeppelzauer M, Ganswindt A, Hensman S, Charlton B: Visualizing sound emission of elephant vocalizations: evidence for two rumble production types. PLoS One 2012.
• [24]Zeppelzauer M, Hensman S, Stoeger S: Towards an automated acoustic detection system for free-ranging elephants. Bioacoustics 2015, 24:13-29.
• [25]Whitehouse A, Irwin P, Gough K: A field guide to the Addo elephants. 2nd edition. Print Grahamstown, Duplin; 2008.
• [26]Stoeger AS, Zeppelzauer M, Baotic A: Age-group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles. Bioacoustics 2014, 23:231-246.
• [27]Wijayakulasooriya JV. Automatic recognition of elephant infrasound calls using formant analysis and hidden markov model. In: Proceedings of the international conference on industrial and information systems. IEEE; 2011. pp. 244–48.
• [28]Venter PJ, Hanekom JJ: Automatic detection of African elephant (Loxodonta africana) infrasonic vocalisations from recordings. Biosyst Eng. 2010, 106:286-294.
• [29]McComb K, Reby D, Baker L, Moss C, Sayialel S: Long-distance communication of acoustic cues to social identity in African elephants. Anim Behav 2003, 66:317-329.
• [30]McComb K, Moss C, Sayialel S, Baker L: Unusually extensive networks of vocal recognition in African elephants. Anim Behav 2000, 59:1103-1109.
• [31]Soltis P, Leighty KA, Wesolek CM, Christina M, Savage A: The expression of affect in African elephants (Loxodonta africana) rumble vocalizations. J Comp Psychol 2009, 123:222-225.
• [32]Soltis J, King LE, Douglas-Hamilton I, Vollrath F, Savage A: African elephant alarm calls distinguish between threats from humans and bees. PLoS One 2014.
• [33]Soltis J, Leong K, Savage A: African elephant vocal communication II: rumble variation reflects individual identity and emotional state of callers. Anim Behav 2005, 70:589-599.
• [34]Leong KM, Ortolani A, Burks K, Mellen JD, Savage A: Quantifying acoustic and temporal characteristics of vocalizations for a group of captive African elephants Loxodonta africana. Bioacoustics 2003, 13:213-231.
• [35]Stoeger-Horwath AS, Stoeger S, Schwammer HM, Kratochvil H: Call repertoire of infant African elephants: first insights into the early vocal ontogeny. J Acoust Soc Am 2007, 121:3922-3931.
• [36]Wood JD, McCowan B, Langbauer W, Viljoen J, Hart L: Classification of African elephant Loxodonta africana rumbles using acoustic parameters and cluster analysis. Bioacoustics 2005, 15:143-161.
• [37]Clemins PJ, Johnson MT. Automatic classification of African elephant (Loxodonta africana) follicular and luteal rumbles. In: 1st conference on Acoustic Communication by Animals. 2003. pp. 27–30.
• [38]Clemins PJ, Johnson MT. Application of speech recognition to African elephant (Loxodonta Africana) vocalizations. Acoustics, Speech, and Signal Processing. 2003. doi:10.1109/ICASSP.2003.1198823.
• [39]Stoeger AS, Charlton BD: Kratochvil, Fitch WT. Vocal cues indicate level of arousal in infant African elephant roars. J Acoust Soc Am 2011, 130:1700-1710.
• [40]Prince JN, Sugumar SJ: Surveillance and tracking of elephants using vocal spectral information. IJRET. 2014, 3:664-671.
• [41]Zeppelzauer M, Stoeger AS, Breiteneder C. Acoustic detection of elephant presence in noisy environments. In: Proceedings of the 2nd ACM International Workshop on Multimedia Analysis for Ecological Data. New York: ACM press; 2013. pp. 3–8.
• [42]Clemins PJ, Trawicki MB, Adi K, Tao J, Johnson MT. Generalized perceptual features for vocalization analysis across multiple species. In: Acoustics, Speech and Signal Processing. IEEE; 2006. doi:10.1109/ICASSP.2006.1660005.
• [43]Cortes C, Vapnik V: Support-vector networks. Machine Learn 1995, 20(3):273-297.
• [44]Hao Y, Campana B, Keogh E: Monitoring and mining animal sounds in visual space. J Insect Behav 2012, 26:466-493.
• [45]Everingham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A: The PASCAL visual object classes challenge: a retrospective. Int J Comput Vision 2015, 111:98-136.
• [46]Ramanan D, Forsyth D, Barnard K: Building models of animals from video. IEEE T Pattern Anal 2006, 28:1319-1334.
• [47]Hannuna SL, Campbell NW, Gibson DP. Identifying quadruped gait in wildlife video. In: Proceeding of the IEEE International Conference on Image Processing, New York: IEEE; 2005. pp. 713–6.
• [48]Lahiri M, Tantipathananandh C, Warungu R, Rubenstein D, Berger-Wolf T. Biometric animal databases from field photographs: Identification of individual zebra in the wild. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval. New York: ACM; 2011. doi:10.1145/1991996.1992002.
• [49]Zeppelzauer M. 2013. Automated detection of elephants in wildlife video. Eurasip J Image Vide. 2013. doi:10.1186/1687-5281-2013-46.
• [50]Christoudias C, Georgescu B, Meer P. Synergism in low level vision. Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada. 2002;4:150–5.
• [51]Poole HJ, Tyack P, Stoeger-Horwath AS, Watwood S: Elephants are capable of vocal learning. Nature 2005, 434:455-456.
• [52]Stoeger AS, Mietchen D, Sukhun O, de Silva S, Herbst CT, Kwon S, Fitch WT: An Asian elephant imitates human speech. Curr Biol 2012, 22:1-5.
• [53]Ananda Kumar, The elephant hills project, http://ncf-india.org/projects/in-the-elephant-hills. Accessed 20th July 2015.
• [54]Plyer A, Le Besnerais G, Champagnat F: Massively parallel Lucas Kanade optical flow for real-time video processing applications. J Real-Time Image Proc 2014.
• [55]Craciun S, Kirchgessner R, George AD, Lam H, Principe JC: A real-time, power-efficient architecture for mean-shift image segmentation. J Real-Time Image Proc 2014.
• [56]Sobral A, Vacavant A: A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Und. 2014, 122:4-21.
• [57]Raspberry PI Foundation. http://www.raspberrypi.org. Accessed 20th July 2015.