<![CDATA[Monitoring a baby's sleep is a critical task for parents, especially when balancing household responsibilities. This study combines the MobileNet-SSD object detection model with the Frame Difference method to analyze sleep movements based on motion thresholds. The system's performance was evaluated by calculating accuracy, precision, recall, and latency, implemented on both laptop and Raspberry Pi devices, and tested using 720p and 480p resolution videos. Results showed accuracy of 82%, precision of 81%, and recall of 92% at 720p, and accuracy of 77%, precision of 80%, and recall of 86% at 480p. However, the Raspberry Pi exhibited a latency of 400ms, 10 times higher than the laptop's 41.28ms latency. Compared to optical flow, this method offers ease of use, and lower computational complexity. The results of this study highlight the impact of resolution on motion detection accuracy, where higher-resolution videos yield more optimal performance. Limitations under low-light conditions suggest potential improvements using deep learning techniques like YOLO and Mediapipe to detect eye conditions. This research contributes to the development of computer vision where the frame differential and object detection methods are proven to provide a fairly high level of accuracy in detecting movement.]]>
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