Cited By
View all- Hamed NRana OOrozco-terWengel PGoossens BPerera C(2024)PoachNet: Predicting Poaching Using an Ontology-Based Knowledge GraphSensors10.3390/s2424814224:24(8142)Online publication date: 20-Dec-2024
Mitigating Wild Animals Poaching Through State-of-the-art Multimedia Data Mining Techniques: A Review
Pages 158 - 172
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.
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View all- Hamed NRana OOrozco-terWengel PGoossens BPerera C(2024)PoachNet: Predicting Poaching Using an Ontology-Based Knowledge GraphSensors10.3390/s2424814224:24(8142)Online publication date: 20-Dec-2024
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