Development of Programmable Camera-Trap

  IJCTT-book-cover
 
         
 
© 2020 by IJCTT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Mehmet Karakose, Canan Tastimur, Selim Özdemir, Merve Erol, Ahmet Tokgoz, Erhan Akin
DOI :  10.14445/22312803/IJCTT-V68I6P111

How to Cite?

Mehmet Karakose, Canan Tastimur, Selim Özdemir, Merve Erol, Ahmet Tokgoz, Erhan Akin, "Development of Programmable Camera-Trap," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 1-13, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I6P111

Abstract
The monitoring of wildlife, especially in large areas, has great difficulties in terms of time, personnel and resources. Different methods and alternatives have been tried to be developed to overcome these difficulties. One of these methods is the camera trap devices. Camera trap devices are used for wildlife monitoring and security reasons. As a result of the widespread use of camera trap, a programmable camera trap device was designed in this study. The developed camera trap device has some advantages over camera trap in the market. In this work, the development of a programmable photo trap for the monitoring of endangered animals and illegal human activities is proposed. In this study, the movement in the area to be monitored is detected by IR rays and the camera is activated. After the camera is activated, the monitored media is photographed. Then, the captured image was compressed and sent to the monitoring center via GSM / GPRS line. One of the advantages of the developed camera trap device is that it provides superiority in cases where the speed of sending the captured photo to the monitoring center should be high. Another one is that, according to camera trap systems, the image can be taken instantly by tolerating the expected time to wake up the device and take the picture, as it instantly saves the image. Because the photo traps are used in the rural area, speed optimization for energy and intervention events has been studied and as a result a different photocopier devices have been designed.

Keywords
Camera, Photo trap, Programmable, Surveillance, Wild environment.

Reference
[1] Y Uçarl? ve B Sa?lam, “Yaban Hayat? Çal??malar?nda Camera trap Kullan?m?”. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 2013; 14(2): 321-331.
[2] FA Pamplin, “Optimising the value of by-catch from Lynx lynx camera trap surveys in the Swiss Jura Region”. M.S. thesis, University of East Anglia, Norwich, England, 2013.
[3] OR Wearn and PG Kapfer, “Camera trapping for conservation: a guide to best practice”. WWF Conservation Technology Series, 2017; 1(1).
[4] EM Soininen., I Jensvoll, ST Killengreen, and RA Ims, “Under the snow: a new camera trap opens the white box of subnivean ecology”. Remote Sensing in Ecology and Conservation, 2015; 1(2): 29-38.
[5] GE Leflore, “Assessing Wild Canid Distribution Using Camera Traps in the Pioneer Valley of Western Massachusetts”. M.S. thesis, Dept. Environmental Conservation University of Massachusetts Amherst, Amherst, United States, 2014.
[6] R Behnke, “A camera-trap based inventory to assess species composition of large- and medium-sized terrestrial mammals in a Lowland Amazonian rainforest in Loreto Peru: a comparison of wet and dry season”. M.S. thesis, University of Bodenkultur Wien, Wien, Austria, 2015.
[7] A Eichholzer, “Testing the applicability of pictures taken by camera-traps for monitoring the European wildcat Felis silvestris silvestris in the Jura Mountains of Switzerland”. M.S. thesis, University of Zürich, Zürich, Switzerland, 2010.
[8] S Nazir, S Newey, R Irvine, F Verdicchio, P Davidson, G Fairhurst, ve R Van Der Wal, “WiseEye: Next generation expandable and programmable camera trap platform for wildlife research”. PloS one, 2017; 12(1).
[9] H Thom, “Uni1ed Detection System for Automatic, Real-Time, Accurate Animal Detection in Camera Trap Images from the Arctic Tundra”. M.S. thesis, The Arctic University of Norway, Tromso, Norway, 2017.
[10] H Hendry and C Mann, “Camelot--Intuitive Software for Camera Trap Data Management”. bioRxiv, 2017.
[11] B Wang, “Automatic Animal Species Identification Based on Camera Trapping Data”. M.S. thesis, University of Alberta, Alberta, Canada, 2014.
[12] A Miguel, J Beard, C Bales-Heisterkamp, and R Bayrakcismith, “Sorting camera trap images”. 2017 IEEE Global Conference on Signal and Information Processing; 2017; Canada: 249-253.
[13] C Zhu, T Li, and G Li, “Towards Automatic Wild Animal Detection in Low Quality Camera-Trap Images Using Two-Channeled Perceiving Residual Pyramid Networks”. IEEE Conference on Computer Vision and Pattern Recognition; 2017; Italy: 2860-2864.
[14] A Miguel, S Beery, E Flores, L Klemesrud, and R Bayrakcismith, “Finding areas of motion in camera trap images”. IEEE International Conference on Image Processing; 2016; USA: 1334-1338.
[15] JH Giraldo-Zuluaga, A Salazar, A Gomez and A Diaz-Pulido, “Recognition of Mammal Genera on Camera-Trap Images Using Multi-layer Robust Principal Component Analysis and Mixture Neural Networks”. IEEE 29th International Conference on Tools with Artificial Intelligence; 2017; USA: 53-60.
[16] L Camacho, R Baquerizo, J Palomino and M Zarzosa, “Deployment of a Set of Camera Trap Networks for Wildlife Inventory in Western Amazon Rainforest”. IEEE Sensors Journal; 2017, 17(2): 8000-8007.
[17] H Yousif, J Yuan, R. Kays and Z. He “Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification”. IEEE International Symposium on Circuits and Systems; 2017; USA: 1-4.
[18] EJ Berkenpas., BS Henning., CM Shepard and AJ Turchik, “The Driftcam: A buoyancy controlled pelagic camera trap”. OCEANS 2013 MTS/IEEE San Diego; 2013; USA: 1-6.
[19] KPK Reddy and R Aravind, “Segmentation of camera-trap tiger images based on texture and color features”. 2012 National Conference on Communications; 2012; India: 1-5.
[20] U. Bolat, “Camera trap Kullan?m Rehber”i. T.C. Orman Su ??leri Bakanl??? Do?a Koruma ve Milli Parklar Genel Müdürlü?ü, Ankara 2017.
[21] C. Ta?timur, H. Yetis, M. Karaköse and E. Ak?n, “Rail Defect Detection and Classification with Real Time Image Processing Technique”. International Journal of Computer Science and Software Engineering; 2016, 5(12): 283-290.
[22] C. Ta?timur, M. Karaköse and E. Ak?n, “A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways”. International Journal of Applied Mathematics, Electronics and Computers; 2016, 4(Special Issue): 319-325.
[23] E. Karakose, M.T. Gencoglu, M. Karakose, I. Aydin and E. Akin, “A new experimental approach using image processing-based tracking for an efficient fault diagnosis in pantograph–catenary systems”. IEEE Transactions on Industrial Informatics; 2017, 13(2): 635-643.
[24] S. Ozdemir, M. Erol and A. Tokgoz, “Yaban Hayat?n? ?zlemede Fotokapan Sistemleri”, Lisans Tezi, F?rat Üniversitesi, Elaz??, Türkiye, 2018. R.Dhanujalakshmi, B.Divya, C.Divya@sandhiya, A.Robertsingh, "Image Processing Based Fire Detection System using Rasperry Pi System" SSRG International Journal of Computer Science and Engineering .4(4), 2017.