GPU上的快速增益自适应KLT跟踪

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI:10.1109/CVPRW.2008.4563089

C. Zach, D. Gallup, Jan-Michael Frahm

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引用次数: 83

摘要

视频输入的高性能特征跟踪在许多计算机视觉技术和混合现实应用中是一个有价值的工具。这项工作提出了一种在GPU上执行KLT特征跟踪的改进和实质上加速的方法。此外，估计连续帧之间的全局增益比以补偿相机曝光的变化。所提出的方法可以在最先进的消费级gpu上实现每秒200帧以上的PAL (720 × 576)分辨率数据，并且即使在低端移动图形处理器上也能提供实时性能。

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Fast gain-adaptive KLT tracking on the GPU

High-performance feature tracking from video input is a valuable tool in many computer vision techniques and mixed reality applications. This work presents a refined and substantially accelerated approach to KLT feature tracking performed on the GPU. Additionally, a global gain ratio between successive frames is estimated to compensate for changes in the camera exposure. The proposed approach achieves more than 200 frames per second on state-of-the art consumer GPUs for PAL (720 times 576) resolution data, and delivers real-time performance even on low-end mobile graphics processors.

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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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