This work proposes a home-centric monitoring system that integrates machine vision, YOLO-based object detection, and household sensing platforms, fixed indoor cameras and mobile robots such as robot vacuums, to track the activities and partial affective indicators of indoor cats. The system targets scenarios in which owners are away for work or travel and is especially relevant for multi-cat households, where social dynamics, resource access, and routine changes can influence wellbeing. By operating primarily on-device, the approach aims to deliver real-time insights while preserving privacy and minimizing bandwidth.

The architecture combines YOLO for fast detection of cats and relevant objects, multi-object tracking and re-identification for per-cat continuity, and posture and action recognition for behavior classification. Temporal analytics convert frame-level outputs into interpretable daily and weekly metrics, including locomotion patterns, play bouts, resource visits, zone occupancy, and trend deviations from individualized baselines. The mobile robot augments fixed viewpoints by patrolling occluded areas and following up on uncertain detections, improving coverage and robustness in cluttered indoor environments.

To estimate partial emotion-related indicators, the system aggregates proxies such as posture cues (ear position, tail carriage, body curvature), activity balance, hiding or exploration patterns, and resource interaction changes, optionally fused with audio or smart-home signals. It highlights potential stress or discomfort when multi-cue deviations persist, while emphasizing that such inferences are approximations and not medical diagnoses. Deployment guidance addresses model selection for edge devices, dataset curation tailored to the home, per-cat baselining, and owner-in-the-loop corrections.