This paper addresses the significant challenge in animal-related disciplines of effectively measuring behavior, particularly with the advent of computational approaches that generate vast amounts of data and offer practically endless measurement possibilities. The authors, including Tom Menaker and Anna Zamansky, propose a data-driven framework called Data-Driven Behavioral Pattern Analysis (DD-BPA). This framework aims to guide researchers in selecting relevant behavioral parameters by applying data mining techniques to extract insights from experimental data, shifting the focus from merely automating manual coding to actively supporting data analysis decisions. They demonstrate their approach through a clustering-based analysis of animal trajectories from a dog facial preference experiment at the Clever Dog Lab, which effectively identified “prevalent areas of stay” for the animal subjects using tools like Blyzer.
Non-Invasive Computer Vision-Based Fruit Fly Larvae Differentiation: Ceratitis capitata and Bactrocera zonata
This paper proposes a novel, non-invasive method using computer vision
