An Integrated Machine Learning Camera for Wildlife Detection

Abstract

Expanding urbanization in California results in increasing isolation and segmentation of wildlife habitats. To understand the problem, biologists want to be able to count the appearance of animals in habitats. A new wildlife camera trap system is being developed, using edge computing to optimize the collection and classification of wildlife images in real time. A waterproofed prototype that will constantly record videos in the wild and process data in real time using trained AI is described along with a process for validation of the new system in direct comparison with typical current methods. In addition, wildlife counts acquired as control data are analyzed and showed that after doing the comparison of six weeks data between the data collected from island and the corridor, the island has the most deer appearance. Knowing the deer’s habitat, this project can now test the AI around that area and decrease intrudes.

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