Published in Sensors on March 1, 2023, the study by **Othmane Atif and colleagues** introduces a **behavior-based video summarization and visualization system** designed to support dog health and welfare monitoring using artificial intelligence.
The system operates through four modules: video collection and preprocessing; image cropping via object detection to isolate the dog; a **behavior recognition engine** powered by EfficientNetV2 and LSTM models; and a final **visualization module** that generates summaries to help assess behavioral trends.
Achieving an **average F1 score of 0.955**, the AI accurately recognizes a wide range of dog behaviors in real time. This includes parsing **motion and appearance features** to detect activity changes, which may signal underlying health or emotional issues.
The system’s ability to **visually summarize location and behavior patterns** allows owners to notice deviations from normal routines—such as increased restlessness, excessive sleeping, or unusual movement—potentially prompting earlier interventions or vet consultations.
By reducing the reliance on frequent vet visits and empowering owners with objective behavioral data, this AI-based tool represents a major advancement in accessible pet care technology.
Source: Atif, O., Lee, J., Park, D., & Chung, Y. (2023). Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23052630
