Transfer Learning for Animal Species Identification from CCTV Image: Case Study Zakouma National Park
Keywords:
CCTV, species, image classification, deep learning, machine learning, Convolution Neural Network, image augmentationAbstract
The precise identification of animal species is of utmost importance in comprehending biodiversity, conducting surveillance of endangered species, and examining the potential ramifications of climate change on the geographical dispersion of species in specific areas. Closed-circuit television (CCTV) cameras represent a form of passive surveillance technology that generates a substantial volume of ecological imagery. The manual evaluation of extensive datasets is a labor-intensive, time-consuming, and costly process, underscoring the importance of automated ecological analysis. In the realm of computer vision, there has been significant progress in addressing issues related to object and species recognition through the development of deep learning networks. These networks have demonstrated state-of-the-art performance, showcasing their effectiveness in this domain. In this study, we have undertaken the development and evaluation of machine learning models with the purpose of classifying animal groups based on camera trap images. Transfer learning was employed in our study to conduct experiments with VGG19, GoogLeNet (InceptionV3), ResNet50, and DenseNet121. CNN-1 achieved an accuracy of 53% in multiclassification, whereas GoogLeNet demonstrated a higher accuracy of 87%. Similarly, ResNet50 earned an accuracy of 83%, DenseNet reached 81%, and VGG achieved 79%. The findings of this study indicate that the utilization of transfer learning yields superior performance compared to that of the self-trained model. The models exhibited encouraging results in terms of species detection and classification within Zakouma National Park.
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