This study introduces a novel paradigm for digitally-enhanced dog behavioral testing, aiming to provide a more objective, robust, and resource-efficient alternative to traditional subjective and time-consuming methods like questionnaires or observation. Researchers tested 53 dogs using a “Stranger Test” protocol, collecting data from three expert behavioral scores and owner-completed Canine Behavioral Assessment and Research Questionnaires (C-BARQ). Utilizing an automated computational approach, specifically the BLYZER system, dog trajectories were extracted and clustered in a “human-free” way from video footage. The unsupervised clustering revealed two distinct behavioral profiles, showing a significant difference in stranger-directed fear C-BARQ scores and effectively separating relaxed dogs from those exhibiting excessive behaviors based on expert evaluations. Furthermore, a machine learning classifier achieved an accuracy of 78% in predicting expert scores of dog coping styles, and a regression model predicted C-BARQ categories with varying success, performing best for Owner-Directed Aggression and Excitability. This research highlights the significant value and promise of AI in the field of dog behavioral assessment, offering a path towards more reliable and objective evaluations.
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
