RVM+: An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design

TL;DR

RVM+ is an AI framework that improves real-time video portrait segmentation with better accuracy and efficiency for applications like AR and robotics.

Contribution

RVM+ introduces ConvGRU and knowledge distillation to enhance temporal consistency and reduce computational costs in video segmentation.

Findings

RVM+ outperforms state-of-the-art methods in segmentation accuracy and temporal consistency.

Knowledge distillation reduces computational demands with minimal accuracy loss.

Key metrics like MIoU, SAD, and dtSSD confirm the model's robustness and efficiency.

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.

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