Can AI interpret pet behaviour based on sound or video ?
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Current systems can classify common animal calls (e.g., dog barks, cat meows) into a handful of coarse categories, achieving accuracies in the 70–90 % range depending on species and dataset, but translating these labels into meaningful interpretations of emotional states or intentions remains unreliable. Video-based pose estimation now tracks animal movement across multiple joints in real time, yet linking body posture or facial expressions to specific feelings or actions is still largely a research problem rather than a production capability. A few start-ups and academic labs offer consumer-grade “bark translators,” but results are largely anecdotal and not clinically validated. Work in welfare science uses machine learning to detect distress calls in livestock barns, but adoption outside niche applications is limited.
— Enriched May 12, 2026 · Source: Tufts University
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Status senest tjekket May 12, 2026.
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