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- W4313597705 abstract "Human-wildlife conflicts in the habitats along the forest fringes are a substantial issue. An automated monitoring system that can find animal breaches and deter them from foraging fields is essential to solve this conflict. However, automatically forefending the intruding animals is a challenging task. In this paper, we propose a deep learning model for elephant identification using YOLO lite with knowledge distillation which could be easily deployed in edge devices. We also propose an elephant re-identification system using Siamese network which is helpful in tracking the number of times the elephant tries to forage the field. This re-encounter information about the same elephant can be used to decide the averting sound for the particular elephant. The proposed system is found to show an accuracy of 89%, which is provides good performance improvement when compared to the state of art models proposed for animal identification. Thus the proposed lite weight knowledge distillation based animal identification model and deep learning based animal re-identification model can be employed in edge devices for real time monitoring and animal deterring to safe guard the farm fields." @default.
- W4313597705 created "2023-01-06" @default.
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- W4313597705 date "2023-01-05" @default.
- W4313597705 modified "2023-09-27" @default.
- W4313597705 title "Computer vision based knowledge distillation model for animal classification and re-identification using Siamese Neural Network" @default.
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- W4313597705 doi "https://doi.org/10.3233/jifs-222672" @default.
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