基于局部邻域像素的快速时空特征点检测方法

Fast Spatial-Temporal Feature Point Detection Based on Local Neighbor Pixels

针对时空特征点检测算法计算效率较低和特征点冗余度较大的问题,提出一种基于邻域像素的快速时空特征点检测方法.通过寻找三维时空中局部邻域内像素值差异较大的点以快速定位时空特征点,然后采用非极大值抑制的方法剔除其中的冗余点,将筛选后的时空特征点用于人体行为识别.此外,还根据二项分布原理研究特征点检测中邻域像素分割阈值的取值范围及其它检测参数优化问题.实验结果表明该算法具有较高的检测速度,既能稳定提取足够数量的特征点又能降低其冗余度,在行为识别中也保持较高的准确率.

To solve the problem of low computational efficiency and many redundant feature points in feature point detection algorithm, a fast spatial-temporal feature point detection algorithm based on local neighbor pixels is proposed. The spatial-temporal feature points are located quickly by finding the points with great difference in pixel value in 3D spatial-temporal local neighborhood. Then the redundant feature points are removed with the 3D non-maxima suppression method, and the screened feature points are applied to human action recognition. In addition, the range of pixel segmentation threshold in local area and other detection problems of parameter optimization are analyzed according to binomial probability distribution principle. The experimental results show that the proposed algorithm not only improves the speed of feature point detection but also reliably detects enough amounts of feature points with the least redundancy, which leads to the high accuracy in human action recognition.