EFFORTS TO CONSTRUCT A BEHAVIOR DETECTION SYSTEM FOR INVESTIGATION OF RAPTORS

Abstract

The breeding status of raptors is surveyed as part of environmental impact assessments for various infrastructure development projects. While fixed point observation is the conventional method for these surveys, recently, monitoring of raptors in nests using video cameras fixed on nesting trees have become popular. However, the video camera method has challenges including the need to shorten the time required to check the video and securing specialized technicians. Thus, to monitor the breeding status of raptors easily and quickly, the authors developed a behavior detection system for goshawks (Accipiter gentili) using image recognition. First, image data of goshawks were extracted from videos recorded at the nest. Next, the image data were classified into three patterns (nesting, egg-laying, and feeding). Then, deep learning was conducted, and AI modeling was done to automatically detect the behavior of goshawks. The model detected the behavior of the goshawk with satisfactory accuracy. Additionally, to pursue DX further, a dashboard was developed to detect the behavior of goshawks quickly.