skip to main content
10.1145/3421558.3421584acmotherconferencesArticle/Chapter ViewAbstractPublication PagesipmvConference Proceedingsconference-collections
research-article

Mitigating Wild Animals Poaching Through State-of-the-art Multimedia Data Mining Techniques: A Review

Authors Info & Claims
Published:25 November 2020Publication History

ABSTRACT

Wild animal poaching, particular rhinos, and elephants in Africa, is a serious destruction for biodiversity and eco-tourism. Governments and numerous Non – Government Organizations (NGOs) spent a great amount of human labor and money every year in preventing poaching. Recently, advanced techniques, like intelligent video surveillance and multimedia data mining, have been adopted to help more efficiently mitigate wild animals poaching. In this paper, we provide a detailed review of the state-of-the-art video surveillance and multimedia data mining techniques for mitigating wild animal poaching from four aspects according to processing steps, namely object detection, object classification, object behavior analysis and invader analysis. More specifically, different algorithms in each aspect are further subdivided into sub-categories and compared in terms of pros, cons, efficiency, and complexity. While these techniques have been thoroughly researched separately, such topics have not been superimposed in the paradigm of wild animals poaching. To the best of our knowledge, this is the first such comprehensive review of the recent advances of the intelligent video understanding and multimedia data mining for mitigating wild animals poaching and hopefully it would help the improvement, implementation, and applications of advanced techniques in preventing wild animal poaching and protecting diverse especially endangered species for the one and only one home for us human being.

References

  1. Ahmed, M.A. 2018. A novel decision tree classification based on post-pruning with Bayes minimum risk. PLoS ONE. 13, 4 (2018), 1–12. DOI:https://doi.org/10.1371/journal.pone.0194168.Google ScholarGoogle ScholarCross RefCross Ref
  2. Amosov, O.S. 2019. Using the ensemble of deep neural networks for normal and abnormal situations detection and recognition in the continuous video stream of the security system. Procedia Computer Science. 150, (2019), 532–539. DOI:https://doi.org/10.1016/j.procs.2019.02.089.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Augusta Kani, G. and Geetha, P. 2018. Object-based region proposal via multiple cues extraction. Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017. 2018-Janua, (2018), 1550–1554. DOI:https://doi.org/10.1109/ICCSP.2017.8286648.Google ScholarGoogle Scholar
  4. Bai, Y. 2018. SOD-MTGAN: Small object detection via multi-task generative adversarial network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11217 LNCS, i (2018), 210–226. DOI:https://doi.org/10.1007/978-3-030-01261-8_13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bell, S. 2016. Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. 33rd International Conference on Machine Learning, ICML 2016. 4, (2016), 2611–2620. DOI:https://doi.org/10.4249/scholarpedia.1888.Google ScholarGoogle Scholar
  6. Bhatt, C.A. and Kankanhalli, M.S. 2011. Multimedia data mining: State of the art and challenges. Multimedia Tools and Applications. 51, 1 (2011), 35–76. DOI:https://doi.org/10.1007/s11042-010-0645-5.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bogomolov, Y. 2003. Classification of Moving Targets Based on Motion and Appearance. Proc. British Machine Vision Conference. 2, (2003), 44.1-44.10. DOI:https://doi.org/10.5244/c.17.44.Google ScholarGoogle ScholarCross RefCross Ref
  8. Bondi, E. 2019. Using Game Theory in Real Time in the Real World: A Conservation Case Study. International Conference on Autonomous Agents and Multiagent Systems. (2019), 0–2.Google ScholarGoogle Scholar
  9. Bonneau, M. 2020. Outdoor animal tracking combining neural network and time-lapse cameras. Computers and Electronics in Agriculture. 168, December 2019 (2020), 105150. DOI:https://doi.org/10.1016/j.compag.2019.105150.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Borwarnginn, P. and Thongkanchorn, K. 2019. Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning. 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE). 7, (2019), 1–5.Google ScholarGoogle Scholar
  11. Choubisa, T. 2018. Human Crawl vs Animal Movement and Person with Object Classifications Using CNN for Side-view Images from Camera. 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018. (2018), 48–54. DOI:https://doi.org/10.1109/ICACCI.2018.8554775.Google ScholarGoogle ScholarCross RefCross Ref
  12. Choubisa, T. 2017. LITE: Light-based Intrusion deTection systEm Using an Optical-Camera and a Single Board Computer. submitted to SenseApp 2017. (2017), 1–3.Google ScholarGoogle Scholar
  13. Christodoulou, C.I. 2003. Texture-based classification of atherosclerotic carotid plaques. IEEE Transactions on Medical Imaging. 22, 7 (2003), 902–912. DOI:https://doi.org/10.1109/TMI.2003.815066.Google ScholarGoogle ScholarCross RefCross Ref
  14. Corcoran, E. 2019. Automated detection of koalas using low-level aerial surveillance and machine learning. Scientific Reports. 9, 1 (2019), 1–9. DOI:https://doi.org/10.1038/s41598-019-39917-5.Google ScholarGoogle ScholarCross RefCross Ref
  15. Dahmane, M. and Meunier, J. 2005. Real-time video surveillance with self-organizing maps. Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005. (2005), 136–143. DOI:https://doi.org/10.1109/CRV.2005.65.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Debeljak, M. 2001. Habitat suitability modelling for red deer (Cervus elaphus L.) in South-central Slovenia with classification trees. Ecological Modelling. 138, (2001), 321–330.Google ScholarGoogle Scholar
  17. Deep Learning in Video Surveillance: 2018. https://www.ee.cuhk.edu.hk/∼xgwang/MSF.pdf. Accessed: 2019-11-21.Google ScholarGoogle Scholar
  18. Dick, A.R. and Brooks, M.J. 2003. Issues in automated visual surveillance. Proc. VIIth Digital Image. January 2003 (2003), 195–204. DOI:https://doi.org/10.1.1.124.2355.Google ScholarGoogle Scholar
  19. Duhart, C. 2019. Deep Learning for Wildlife Conservation and Restoration Efforts. International Conference on Machine Learning. June (2019).Google ScholarGoogle Scholar
  20. Dynamic Time Warping with Time Series: 2018. https://medium.com/@shachiakyaagba_41915/dynamic-time-warping-with-time-series-1f5c05fb8950. Accessed: 2020-02-08.Google ScholarGoogle Scholar
  21. van Eeden, W.D. 2018. Micro-Doppler radar classification of humans and animals in an operational environment. Expert Systems with Applications. 102, (2018), 1–11. DOI:https://doi.org/10.1016/j.eswa.2018.02.019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. El-Henawy, I. 2017. A New Muzzle Classification Model Using Decision Tree Classifier. International Journal of Electronics and Information Engineering. 6, 1 (2017), 12–24. DOI:https://doi.org/10.6636/IJEIE.201703.6(1).02).Google ScholarGoogle Scholar
  23. Ellwart, D. and Czyzewski, A. 2011. Viewpoint independent shape-based object classification for video surveillance. International Workshop on Image Analysis for Multimedia Interactive Services. (2011).Google ScholarGoogle Scholar
  24. Erhan, D. 2014. Scalable object detection using deep neural networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (2014), 2155–2162. DOI:https://doi.org/10.1109/CVPR.2014.276.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Fang, F. 2016. Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security. Association for the Advancement of Artificial Intelligence. (2016).Google ScholarGoogle Scholar
  26. Fang, F. 2017. Predicting Poaching for Wildlife Protection. IBM Journal of Research and Development. 61, 6 (2017), 1–21.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Fang, F. 2015. When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing. In International Joint Conference on Artificial Intelligence (IJCAI). (2015).Google ScholarGoogle Scholar
  28. Fang, F. 2015. When security games go green: Designing defender strategies to prevent poaching and illegal fishing. IJCAI International Joint Conference on Artificial Intelligence. 2015-Janua, (2015), 2589–2595.Google ScholarGoogle Scholar
  29. Fang, F.E.I. and Nguyen, T.H. 2016. Green Security Games: Apply Game Theory to Addressing Green Security Challenges. ACM SIGecom Exchanges. 15, 1 (2016), 78–83.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Fernandes, D.A.B. 2017. Applications of artificial immune systems to computer security: A survey. Journal of Information Security and Applications. 35, (2017), 138–159. DOI:https://doi.org/10.1016/j.jisa.2017.06.007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Feyereisl, J. and Aickelin, U. 2016. SelfOrganising Maps in Computer Security. Computer Security: Intrusion, Detection and Prevention. (2016), 1–30.Google ScholarGoogle Scholar
  32. Gholami, S. 2018. Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers ( Corrected Version ). 17th International Conference on Autonomous Agents and Multiagent Systems. (2018).Google ScholarGoogle Scholar
  33. Girshick, R. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1, (2014), 5000. DOI:https://doi.org/10.1109/CVPR.2014.81.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Grewal, J.K. 2019. Markov models — hidden Markov models. Nature Methods. 16, 9 (2019), 795–796. DOI:https://doi.org/10.1038/s41592-019-0532-6.Google ScholarGoogle ScholarCross RefCross Ref
  35. He, K. 2015. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37, 9 (2015), 1904–1916. DOI:https://doi.org/10.1109/TPAMI.2015.2389824.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Heitz, G. 2009. Shape-based object localization for descriptive classification. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. (2009), 633–640.Google ScholarGoogle Scholar
  37. Hospedales, T. 2010. A Markov Clustering Topic Model for mining behaviour in video. 2009 IEEE 12th International Conference on Computer Vision. Iccv (2010), 1165–1172. DOI:https://doi.org/10.1109/iccv.2009.5459342.Google ScholarGoogle Scholar
  38. Hu, W. 2004. A Survey on Visual Surveillance of Object Motion and Behavior. IEEE Expert Systems with Applications. 34, 3 (2004), 334–352. DOI:https://doi.org/10.1109/TSMCC.2004.829274.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Huang, X. 2019. An improved single shot multibox detector method applied in body condition score for dairy cows. Animals - MDPI. 9, 7 (2019). DOI:https://doi.org/10.3390/ani9070470.Google ScholarGoogle Scholar
  40. Introduction to Decision Trees: 2018. https://medium.com/greyatom/decision-trees-a-simple-way-to-visualize-a-decision-dc506a403aeb. Accessed: 2019-12-31.Google ScholarGoogle Scholar
  41. Jakkula, V. 2011. Tutorial on Support Vector Machine (SVM). School of EECS, Washington State University. (2011), 1–13.Google ScholarGoogle Scholar
  42. Ji, X. and Liu, H. 2010. Advances in view-invariant human motion analysis: A review. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews. 40, 1 (2010), 13–24. DOI:https://doi.org/10.1109/TSMCC.2009.2027608.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Joshi, R.C. 2018. Object detection, classification and tracking methods for video surveillance: A review. 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018. (2018), 1–7. DOI:https://doi.org/10.1109/CCAA.2018.8777708.Google ScholarGoogle ScholarCross RefCross Ref
  44. Kamavisdar, P. 2013. A Survey on Image Classification Approaches and Techniques. International Journal of Advanced Research in Computer and Communication Engineering. 2, 1 (2013), 1005–1009.Google ScholarGoogle Scholar
  45. Kar, D. 2017. Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data. Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems. (2017), 159–167.Google ScholarGoogle Scholar
  46. Khan, R. 2012. COLOR BASED SKIN CLASSIFICATION. Information Engineering and Computer Science. March (2012).Google ScholarGoogle Scholar
  47. Kong, Y. and Wang, L. 2010. Moving target classification in video sequences based on features combination and SVM. 2010 International Conference on Computational Intelligence and Software Engineering, CiSE 2010. 2 (2010), 1–4. DOI:https://doi.org/10.1109/CISE.2010.5676969.Google ScholarGoogle ScholarCross RefCross Ref
  48. Kuettel, D. 2010. What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes. Proc. IEEE International Conference of Computer Vision. (2010), 1951–1958.Google ScholarGoogle Scholar
  49. Lazebnik, S. 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. Cvpr. (2006).Google ScholarGoogle Scholar
  50. Leos-Barajas, V. 2017. Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures. Journal of Agricultural, Biological, and Environmental Statistics. 22, 3 (2017), 232–248. DOI:https://doi.org/10.1007/s13253-017-0282-9.Google ScholarGoogle ScholarCross RefCross Ref
  51. Li, D. 2015. A negative selection algorithm with online adaptive learning under small samples for anomaly detection. Neurocomputing. 149, PB (2015), 515–525. DOI:https://doi.org/10.1016/j.neucom.2014.08.022.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Li, S. 2018. Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (2018), 5457–5466. DOI:https://doi.org/10.1109/CVPR.2018.00572.Google ScholarGoogle ScholarCross RefCross Ref
  53. Li, W. 2014. Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36, 1 (2014), 18–32. DOI:https://doi.org/10.1109/TPAMI.2013.111.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Liensberger, C. 2009. Color-based and context-aware skin detection for online video annotation. 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP ’09. November (2009). DOI:https://doi.org/10.1109/MMSP.2009.5293337.Google ScholarGoogle ScholarCross RefCross Ref
  55. Liu, W. 2016. SSD: Single Shot MultiBox Detector. European Conference on Computer Vision. 1, (2016), 852–869. DOI:https://doi.org/10.1007/978-3-319-46448-0.Google ScholarGoogle Scholar
  56. Lovell, B.C. and Walder, C.J. 2005. Support vector machines for business applications. Business Applications and Computational Intelligence. (2005), 267–290. DOI:https://doi.org/10.4018/978-1-59140-702-7.ch014.Google ScholarGoogle Scholar
  57. Maddalena, L. and Petrosino, A. 2008. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing. 17, 7 (2008), 1168–1177. DOI:https://doi.org/10.1109/TIP.2008.924285.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Mao, H. 2017. Shape-based object classification and recognition through continuum manipulation. IEEE International Conference on Intelligent Robots and Systems. 2017-Septe, (2017), 456–463. DOI:https://doi.org/10.1109/IROS.2017.8202193.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Matuska, S. 2014. A novel system for automatic detection and classification of animal. 10th International Conference, ELEKTRO 2014 - Proceedings. (2014), 76–80. DOI:https://doi.org/10.1109/ELEKTRO.2014.6847875.Google ScholarGoogle ScholarCross RefCross Ref
  60. Matuska, S. 2014. Classification of Wild Animals Based on SVM and Local Descriptors. AASRI Conference on Circuits and Signal Processing. (2014).Google ScholarGoogle Scholar
  61. Menezes, R.S.T. de 2016. Object Recognition Using Convolutional Neural Networks. IntechOpen.Google ScholarGoogle Scholar
  62. Mici, L. 2018. A self-organizing neural network architecture for learning human-object interactions. Neurocomputing. 307, December 2019 (2018), 14–24. DOI:https://doi.org/10.1016/j.neucom.2018.04.015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Nawaz, M.A. 2015. Animal Classification in Wildlife Through Images Using Statistical Methods and Decision Tree.Google ScholarGoogle Scholar
  64. Nguyen, T.H. 2016. CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection. Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016). (2016).Google ScholarGoogle Scholar
  65. Norouzzadeh, M.S. 2018. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America. 115, 25 (2018), E5716–E5725. DOI:https://doi.org/10.1073/pnas.1719367115.Google ScholarGoogle ScholarCross RefCross Ref
  66. Okafor, E. 2018. Detection and Recognition of Badgers Using Deep Learning. 27th International Conference on Artificial Neural Networks. November (2018), 554–563. DOI:https://doi.org/10.1007/978-3-030-01424-7.Google ScholarGoogle Scholar
  67. Ouivirach, K. and Dailey, M.N. 2010. Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system. ECTI-CON 2010 - The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. (2010), 884–888.Google ScholarGoogle Scholar
  68. Ovhal, K.B. 2018. Analysis of anomaly detection techniques in video surveillance. Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017. Iciss (2018), 596–601. DOI:https://doi.org/10.1109/ISS1.2017.8389240.Google ScholarGoogle Scholar
  69. Parham, J. 2018. An Animal Detection Pipeline for Identification. Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018. 2018-Janua, (2018), 1075–1083. DOI:https://doi.org/10.1109/WACV.2018.00123.Google ScholarGoogle ScholarCross RefCross Ref
  70. Parisi, G.I. 2017. Lifelong learning of human actions with deep neural network self-organization. Neural Networks. 96, (2017), 137–149. DOI:https://doi.org/10.1016/j.neunet.2017.09.001.Google ScholarGoogle ScholarCross RefCross Ref
  71. Pavithra, G. 2017. Real-Time Color Classification of Objects from Video Streams. 2nd IEEE- RTEICT. (2017), 1683–1686.Google ScholarGoogle ScholarCross RefCross Ref
  72. Perronnin, F. 2010. Improving the Fisher Kernel for Large-Scale Image Classification. ECCV. (2010).Google ScholarGoogle Scholar
  73. Petrushin, V.A. and Khan, L. (Eds) 2007. Multimedia Data Mining and Knowledge Discovery. Springer.Google ScholarGoogle Scholar
  74. Pita, J. 2009. Security applications. ACM SIGecom Exchanges. 8, 2 (2009), 1–4. DOI:https://doi.org/10.1145/1980522.1980527.Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Ramdane, C. and Chikhi, S. 2017. Negative selection algorithm: recent improvements and its application in intrusion detection system. Int. J. Comput. Acad. Res.(IJCAR). 6, 2 (2017), 20–30.Google ScholarGoogle Scholar
  76. Redmon, J. 2016. (YOLO) You Only Look Once. Cvpr. (2016). DOI:https://doi.org/10.1109/CVPR.2016.91.Google ScholarGoogle Scholar
  77. Redmon, J. 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem, (2016), 779–788. DOI:https://doi.org/10.1109/CVPR.2016.91.Google ScholarGoogle ScholarCross RefCross Ref
  78. Redmon, J. and Farhadi, A. 2017. YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua, (2017), 6517–6525. DOI:https://doi.org/10.1109/CVPR.2017.690.Google ScholarGoogle Scholar
  79. Ren, S. 2017. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39, 6 (2017), 1137–1149. DOI:https://doi.org/10.1109/TPAMI.2016.2577031.Google ScholarGoogle ScholarDigital LibraryDigital Library
  80. Robert, B. 2009. Evaluation of three-dimensional accelerometers to monitor and classify behavior patterns in cattle. Computers and Electronics in Agriculture. 67, 1–2 (2009), 80–84. DOI:https://doi.org/10.1016/j.compag.2009.03.002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Roy, S. 2010. A Survey of Game Theory as Applied to Network Security. 43rd Hawaii International Conference on System Sciences. June 2014 (2010). DOI:https://doi.org/10.1109/HICSS.2010.35.Google ScholarGoogle ScholarCross RefCross Ref
  82. Rubin, K.S. and Goldberg, A. 1992. Object Behavior Analysis. Communications of the ACM. 35, 9 (1992), 48–62. DOI:https://doi.org/10.1145/130994.130996.Google ScholarGoogle ScholarDigital LibraryDigital Library
  83. Schmidt, B.H. 2017. Artificial Immune Systems: Applications, Multi Class Classification, Optimizations, and Analysis. ProQuest Dissertations and Theses. (2017), 183.Google ScholarGoogle Scholar
  84. Schofield, D. 2019. Chimpanzee face recognition from videos in the wild using deep learning. Science Advances. 5, 9 (2019), 1–10. DOI:https://doi.org/10.1126/sciadv.aaw0736.Google ScholarGoogle ScholarCross RefCross Ref
  85. Self Organizing Maps: 2018. https://towardsdatascience.com/self-organizing-maps-ff5853a118d4. Accessed: 2020-02-05.Google ScholarGoogle Scholar
  86. Sempena, S. 2011. Human action recognition using Dynamic Time Warping. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. July (2011). DOI:https://doi.org/10.1109/ICEEI.2011.6021605.Google ScholarGoogle ScholarCross RefCross Ref
  87. Senin, P. 2008. Dynamic Time Warping Algorithm Review. Science. 2007, December (2008), 1–23. DOI:https://doi.org/10.1109/IEMBS.2007.4353810.Google ScholarGoogle Scholar
  88. Sergyán, S. 2007. Color Content-based Image Classification. 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics. (2007), 427–434.Google ScholarGoogle Scholar
  89. Shalika, A.W.D.U. and Seneviratne, L. 2016. Animal Classification System Based on Image Processing & Support Vector Machine. Journal of Computer and Communications. 04, 01 (2016), 12–21. DOI:https://doi.org/10.4236/jcc.2016.41002.Google ScholarGoogle ScholarCross RefCross Ref
  90. Sharma, N. 2018. An Analysis of Convolutional Neural Networks for Image Classification. Procedia Computer Science. 132, Iccids (2018), 377–384. DOI:https://doi.org/10.1016/j.procs.2018.05.198.Google ScholarGoogle ScholarDigital LibraryDigital Library
  91. Soltani-Sarvestani, M.A. and Zohreh, A. 2015. Batch color classification using bag of colors and discriminative sparse coding. 2015 2nd International Conference on Pattern Recognition and Image Analysis, IPRIA 2015. Ipria (2015), 1–5. DOI:https://doi.org/10.1109/PRIA.2015.7161620.Google ScholarGoogle Scholar
  92. Szegedy, C. 2016. Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem, (2016), 2818–2826. DOI:https://doi.org/10.1109/CVPR.2016.308.Google ScholarGoogle ScholarCross RefCross Ref
  93. Taheri, S. and Toygar, Ö. 2018. Animal classification using facial images with score-level fusion. IET Computer Vision. 12, 5 (2018), 679–685. DOI:https://doi.org/10.1049/iet-cvi.2017.0079.Google ScholarGoogle ScholarDigital LibraryDigital Library
  94. Tekalp, A.M. 2009. Video Segmentation. The Essential Guide to Video Processing. (2009), 141–173. DOI:https://doi.org/10.1016/B978-0-12-374456-2.00007-4.Google ScholarGoogle Scholar
  95. Thomsen, M.S. 2016. To include or not to include (the invader in community analysis)? That is the question. Biological Invasions. (2016). DOI:https://doi.org/10.1007/s10530-016-1102-9.Google ScholarGoogle Scholar
  96. Thyagarajmurthy A., Ninad M.G., Rakesh B.G., Niranjan S., M.B. 2019. Anomaly Detection in Surveillance Video Using Pose Estimation.Google ScholarGoogle Scholar
  97. Using drones to protect elephants and rhinos in Africa: 2018. .Google ScholarGoogle Scholar
  98. Verdaguer, S.L. 2009. Color Based Image Classification and Description. Universitat Politecnica De Catalunya.Google ScholarGoogle Scholar
  99. Wankhade, P. and Wadhe, P.A.P. 2014. Survey on Analysis of Various Techniques for Multimedia Data Mining. 2, 3 (2014), 137–142.Google ScholarGoogle Scholar
  100. Yi, J. 2019. ASSD: Attentive single shot multibox detector. Computer Vision and Image Understanding. 189, (2019). DOI:https://doi.org/10.1016/j.cviu.2019.102827.Google ScholarGoogle ScholarDigital LibraryDigital Library
  101. Yousif, H. 2017. Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification. Proceedings - IEEE International Symposium on Circuits and Systems. (2017). DOI:https://doi.org/10.1109/ISCAS.2017.8050762.Google ScholarGoogle ScholarCross RefCross Ref
  102. Zhang, C. and Zhang, Z. 2010. A Survey of Recent Advances in Face Detection. MSR-TR-2010-66. 1000, (2010), 53–69.Google ScholarGoogle Scholar
  103. Zhang, T. 2015. Detecting kangaroos in the wild: The first step towards automated animal surveillance. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2015-Augus, (2015), 1961–1965. DOI:https://doi.org/10.1109/ICASSP.2015.7178313.Google ScholarGoogle ScholarCross RefCross Ref
  104. Zhang, Z. 2016. Animal Detection from Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification. IEEE Transactions on Multimedia. (2016). DOI:https://doi.org/10.1109/TMM.2016.2594138.Google ScholarGoogle ScholarCross RefCross Ref
  105. Zhao, Z.Q. 2018. Object Detection with Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems. 30, 11 (2018), 3212–3232. DOI:https://doi.org/10.1109/TNNLS.2018.2876865.Google ScholarGoogle ScholarCross RefCross Ref
  106. Zhu, C. 2017. CMS-RCNN: Contextual multi-scale region-based CNN for unconstrained face detection. Advances in Computer Vision and Pattern Recognition. PartF1, (2017), 57–79. DOI:https://doi.org/10.1007/978-3-319-61657-5_3.Google ScholarGoogle Scholar
  107. Zin, T.T. 2017. A General Video Surveillance Framework for Animal Behavior Analysis. Proceedings - 2016 3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016. (2017), 130–133. DOI:https://doi.org/10.1109/CMCSN.2016.55.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    IPMV '20: Proceedings of the 2020 2nd International Conference on Image Processing and Machine Vision
    August 2020
    194 pages
    ISBN:9781450388412
    DOI:10.1145/3421558

    Copyright © 2020 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 25 November 2020

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited
  • Article Metrics

    • Downloads (Last 12 months)29
    • Downloads (Last 6 weeks)7

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format