Abstract
Wildlife monitoring and analysis are an active research field since last many decades. In this paper, we focus on wildlife monitoring and analysis through animal detection from natural scenes acquired by camera-trap networks. The image sequences obtained from camera-trap consist of highly cluttered images that hinder the detection of animal resulting in low-detection rates and high false discovery rates. To handle this problem, we have used a camera-trap database that has candidate animal proposals using multilevel graph cut in the spatiotemporal domain. These proposals are used to create a verification phase that identifies whether a given patch is animal or background. We have designed animal detection model using self-learned Deep Convolutional Neural Network (DCNN) features. This efficient feature set is then used for classification using state-of-the-art machine learning algorithms, namely support vector machine, k-nearest neighbor, and ensemble tree. Our intensive results show that our detection model using DCNN features provides accuracy of 91.4% on standard camera-trap dataset.
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Verma, G.K., Gupta, P. (2018). Wild Animal Detection Using Deep Convolutional Neural Network. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_27
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DOI: https://doi.org/10.1007/978-981-10-7898-9_27
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