Feature tracking and matching in video using programmable graphics hardware

Abstract This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based Send offprint requests to: Present address: Insert the address here if needed 

• Type: Journal
• Year: 2011
• Where: MVA
• Authors: Sudipta N. Sinha, Jan-Michael Frahm, Marc Pollefeys, Yakup Genc
• Keywords: Computer Vision, GPU-based KLT Implementation, Modern Programmable Graphics, MVA 2011, Standard Cpu Implementations