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.
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- Deep Learning in Video Surveillance: 2018. https://www.ee.cuhk.edu.hk/∼xgwang/MSF.pdf. Accessed: 2019-11-21.Google Scholar
- 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 Scholar
- Duhart, C. 2019. Deep Learning for Wildlife Conservation and Restoration Efforts. International Conference on Machine Learning. June (2019).Google Scholar
- Dynamic Time Warping with Time Series: 2018. https://medium.com/@shachiakyaagba_41915/dynamic-time-warping-with-time-series-1f5c05fb8950. Accessed: 2020-02-08.Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- Fang, F. 2016. Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security. Association for the Advancement of Artificial Intelligence. (2016).Google Scholar
- Fang, F. 2017. Predicting Poaching for Wildlife Protection. IBM Journal of Research and Development. 61, 6 (2017), 1–21.Google ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Feyereisl, J. and Aickelin, U. 2016. SelfOrganising Maps in Computer Security. Computer Security: Intrusion, Detection and Prevention. (2016), 1–30.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- Introduction to Decision Trees: 2018. https://medium.com/greyatom/decision-trees-a-simple-way-to-visualize-a-decision-dc506a403aeb. Accessed: 2019-12-31.Google Scholar
- Jakkula, V. 2011. Tutorial on Support Vector Machine (SVM). School of EECS, Washington State University. (2011), 1–13.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- Khan, R. 2012. COLOR BASED SKIN CLASSIFICATION. Information Engineering and Computer Science. March (2012).Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- Lazebnik, S. 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. Cvpr. (2006).Google Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Matuska, S. 2014. Classification of Wild Animals Based on SVM and Local Descriptors. AASRI Conference on Circuits and Signal Processing. (2014).Google Scholar
- Menezes, R.S.T. de 2016. Object Recognition Using Convolutional Neural Networks. IntechOpen.Google Scholar
- 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 ScholarDigital Library
- Nawaz, M.A. 2015. Animal Classification in Wildlife Through Images Using Statistical Methods and Decision Tree.Google Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- Pavithra, G. 2017. Real-Time Color Classification of Objects from Video Streams. 2nd IEEE- RTEICT. (2017), 1683–1686.Google ScholarCross Ref
- Perronnin, F. 2010. Improving the Fisher Kernel for Large-Scale Image Classification. ECCV. (2010).Google Scholar
- Petrushin, V.A. and Khan, L. (Eds) 2007. Multimedia Data Mining and Knowledge Discovery. Springer.Google Scholar
- Pita, J. 2009. Security applications. ACM SIGecom Exchanges. 8, 2 (2009), 1–4. DOI:https://doi.org/10.1145/1980522.1980527.Google ScholarDigital Library
- 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 Scholar
- Redmon, J. 2016. (YOLO) You Only Look Once. Cvpr. (2016). DOI:https://doi.org/10.1109/CVPR.2016.91.Google Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Schmidt, B.H. 2017. Artificial Immune Systems: Applications, Multi Class Classification, Optimizations, and Analysis. ProQuest Dissertations and Theses. (2017), 183.Google Scholar
- 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 ScholarCross Ref
- Self Organizing Maps: 2018. https://towardsdatascience.com/self-organizing-maps-ff5853a118d4. Accessed: 2020-02-05.Google Scholar
- 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 ScholarCross Ref
- Senin, P. 2008. Dynamic Time Warping Algorithm Review. Science. 2007, December (2008), 1–23. DOI:https://doi.org/10.1109/IEMBS.2007.4353810.Google Scholar
- Sergyán, S. 2007. Color Content-based Image Classification. 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics. (2007), 427–434.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- Thyagarajmurthy A., Ninad M.G., Rakesh B.G., Niranjan S., M.B. 2019. Anomaly Detection in Surveillance Video Using Pose Estimation.Google Scholar
- Using drones to protect elephants and rhinos in Africa: 2018. .Google Scholar
- Verdaguer, S.L. 2009. Color Based Image Classification and Description. Universitat Politecnica De Catalunya.Google Scholar
- Wankhade, P. and Wadhe, P.A.P. 2014. Survey on Analysis of Various Techniques for Multimedia Data Mining. 2, 3 (2014), 137–142.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Zhang, C. and Zhang, Z. 2010. A Survey of Recent Advances in Face Detection. MSR-TR-2010-66. 1000, (2010), 53–69.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
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