Research Paper#Computer Vision, Video Prediction, UAVs, Deep Learning🔬 ResearchAnalyzed: Jan 4, 2026 00:14
RAPTOR: Real-Time High-Resolution Video Prediction for UAVs
Published:Dec 25, 2025 15:12
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for real-time, high-resolution video prediction in autonomous UAVs, a domain where latency is paramount. The authors introduce RAPTOR, a novel architecture designed to overcome the limitations of existing methods that struggle with speed and resolution. The core innovation, Efficient Video Attention (EVA), allows for efficient spatiotemporal modeling, enabling real-time performance on edge hardware. The paper's significance lies in its potential to improve the safety and performance of UAVs in complex environments by enabling them to anticipate future events.
Key Takeaways
- •RAPTOR introduces a novel architecture for real-time, high-resolution video prediction.
- •Efficient Video Attention (EVA) is the core innovation, enabling efficient spatiotemporal modeling.
- •RAPTOR achieves state-of-the-art performance on multiple datasets and edge hardware.
- •The system significantly improves mission success rates in real-world UAV navigation tasks.
Reference
“RAPTOR is the first predictor to exceed 30 FPS on a Jetson AGX Orin for $512^2$ video, setting a new state-of-the-art on UAVid, KTH, and a custom high-resolution dataset in PSNR, SSIM, and LPIPS. Critically, RAPTOR boosts the mission success rate in a real-world UAV navigation task by 18%.”