RVM+: An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design
Year: 2025
Type: article
Abstract: Video portrait segmentation is essential for intelligent sensing systems, including human-computer interaction, autonomous navigation, and augmented reality. However, dynamic video environments introduce significant challenges, such as temporal variations, occlusions, and computational constraints. This study introduces RVM+, an enhanced video segmentation framework based on the Robust Video Matting (RVM) architecture. By incorporating Convolutional Gated Recurrent Units (ConvGRU), RVM+ improves temporal consistency and captures intricate temporal dynamics across video frames. Additionally, a novel knowledge distillation strategy reduces computational demands while maintaining high segmentation accuracy, making the framework ideal for real-time applications in resource-constrained environments. Comprehensive evaluations on challenging datasets show that RVM+ outperforms state-of-the-art methods in both segmentation accuracy and temporal consistency. Key performance indicators such as MIoU, SAD, and dtSSD effectively verify the robustness and efficiency of the model. The integration of knowledge distillation ensures a streamlined and effective design with negligible accuracy trade-offs, highlighting its suitability for practical deployment. This study makes significant strides in intelligent sensor technology, providing a high-performance, efficient, and scalable solution for video segmentation. RVM+ offers potential for applications in fields such as augmented reality, robotics, and real-time video analysis, while also advancing the development of AI-enabled vision sensors.
Source: Sensors
DOI: 10.3390/s25051278
Authors: Na Tang, Yuehui Liao, Yu Chen, Guang Yang, Xiaobo Lai, Jing Chen
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1285418384, G2597775472, Fully Automatic Segmentation and Assessment of Atrial Scars for Atrial Fibrillation Patients
Using LGE MRI, G6756521213, G7986630670, Synergistic Inverse Problems Omni-Solver for Expeditious High Quality Multimodal Cardiovascular MRI via Deep Compressive Sensing and Data Coalescing, G9001168344, G9001168361, G9001168784, G9001168808, G9001168947
OpenAlex ID: W4407792947