A new feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

中文翻译：

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用于改进视觉跟踪的基于描述符的高级特征检测器

本文提出了一种新的带有描述符的特征检测器，即简单鲁棒特征（SRF）算法。在具有 SRF 的 SRF、具有 SURF 的加速鲁棒特征 (SURF)、具有 SURF 的最大稳定极值区域 (MSER)、具有快速视网膜关键点 (FREAK) 的 Harris、具有 FREAK 的最小特征值、来自加速段测试的特征之间进行性能比较(FAST) 与 FREAK，以及二进制稳健不变可扩展关键点 (BRISK) 与 FREAK。在准确性和计算成本方面的性能评估中使用了视觉跟踪数据集。结果表明，将 SRF 检测器与 SRF 描述符相结合是更可取的，因为它平均具有最高的准确度。此外，SRF 与 SRF 的计算成本远低于其他。