An Object-Level Semantic Video Transmission Framework for IoMT Based on Lightweight YOLO and Motion Triggering

In Internet of Multimedia Things (IoMT) environments, video capture and frame transmission consume substantial sensor energy and impose considerable network overhead because of redundant data transmission. Traditional approaches often rely on repeated pixel-level operations and frequent execution of deep segmentation models, thereby increasing computational and transmission costs. This paper proposes an Object-Level Semantic Video Transmission Framework (OSVT) for resource-constrained IoMT environments in which high-definition visual data must be transmitted over low-bandwidth wireless multimedia sensor networks (WMSNs). The proposed OSVT incorporates a dual-stage wake-up mechanism that remains in sleep mode during static scenes, thereby reducing transmission load. The high-power YOLOv11-seg module is activated only when motion exceeds a predefined pixel threshold. Upon detecting an object of interest, the system adopts a hybrid transmission strategy. If the object is recognized from a prior frame, only spatiotemporal metadata are transmitted; otherwise, pixel data for the new object are sent. Rather than performing segmentation for every frame, the method tracks the centroids of detected bounding boxes across consecutive frames to estimate object motion. This mechanism reduces latency and bandwidth consumption. The framework was evaluated through simulations using video sequences representing diverse streaming scenarios. Performance was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and the Semantic Information Index (SII). The results indicate that OSVT can support energy-efficient semantic video transmission in resource-constrained IoMT systems while maintaining reconstruction quality.