基于FAST角点检测的局部鲁棒特征

Local Robust Feature Based on FAST Corner Detection

  • 摘要: 针对目前流行的SIFT、SURF等局部特征存在运算复杂、匹配及后续处理实时性差等问题,在FAST角点检测的基础上,提出了一种新的视觉跟踪特征算法. 该算法能克服实际应用中噪声及室外光照变化的影响,并能快速匹配特征点实现实时处理. 实验结果表明,该视觉跟踪特征算法具备运算量小、实时性高的特点,并且能保证匹配精度及鲁棒性优于原有的视觉跟踪特征.

     

    Abstract: The popular SIFT, SURF and other local features exist computational complexity, poor real-time performance for matching and other follow-up steps. Therefore, a novel visual tracking feature algorithm is proposed based on the FAST corner detection, in order to overcome the impact of noise and outdoor lighting changes in the practical application and, which can quickly match the feature points. Experiments indicate that the proposed tracking feature can ensure the better matching accuracy and robustness than the original visual tracking features, with the lower computational and real-time processing.

     

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