基于高帧率序列的光流估计

Sukhwan Lim, A. Gamal
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引用次数: 50

摘要

基于梯度的光流估计方法,如Lucas-Kanade(1981)方法,对于小位移的场景效果很好,但当物体以大位移移动时就失效了。基于分层匹配的方法不受大位移的影响,但精度较低。利用CMOS图像传感器的高速成像能力,可以提高帧率,在大范围的场景速度下实时获得更精确的光流。此外,通过将存储器和处理与同一芯片上的传感器集成,可以在不过度增加片外数据速率的情况下执行使用高帧率序列的光流估计。本文介绍了一种利用高帧率序列在标准帧率下获得高精度光流的方法。首先利用Lucas-Kanade方法得到高帧率下的光流估计,然后对这些估计进行累加和改进,得到标准帧率下的光流估计。对透视扭曲合成的视频序列进行了实验。结果表明,在适度的内存和计算能力要求下,光流估计精度有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical flow estimation using high frame rate sequences
Gradient-based optical flow estimation methods such as the Lucas-Kanade (1981) method work well for scenes with small displacements but fail when objects move with large displacements. Hierarchical matching-based methods do not suffer from large displacements but are less accurate. By utilizing the high speed imaging capability of CMOS image sensors, the frame rate can be increased to obtain more accurate optical flow with wide range of scene velocities in real time. Further, by integrating the memory and processing with the sensor on the same chip, optical flow estimation using high frame rate sequences can be performed without unduly increasing the off-chip data rate. The paper describes a method for obtaining high accuracy optical flow at a standard frame rate using high frame rate sequences. The Lucas-Kanade method is used to obtain optical flow estimates at high frame rate, which are then accumulated and refined to obtain optical flow estimates at a standard frame rate. The method is tested on video sequences synthetically generated by perspective warping. The results demonstrate significant improvements in optical flow estimation accuracy with moderate memory and computational power requirements.
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