面向移动增强现实的室外阴影实时检测技术

Real time outdoor shadow detection technology for mobile augmented reality

针对目前移动视点下视频阴影检测算法存在的误检测率高和边缘连续性差的问题,提出了一种基于边跟踪、边检测框架的实时阴影检测算法。首先对前后2帧重叠的阴影部分进行2次光流跟踪,并筛选掉前后向跟踪误差较大的点,通过Canny边缘置信保证跟踪边缘的准确性;然后通过基于光流的区域划分法得到待检测的新增区域;其次,针对纹理边缘误检测、软阴影检测和暗黑区域误检测等问题构建了七维特征向量,且通过提取阴影边缘特征向量训练支持向量机(SVM)分类器,以检测新增区域中的阴影;最后对于检测结果中存在的断边,提出一种基于RGB颜色空间梯度方向一致性算法对断边进行优化连接。实验结果表明与最新的研究成果相比该算法综合性能最高,在检测准确性和边缘连续性方面优于现有方法。

Aiming at the problems of high false detection rate and poor edge continuity in current video shadow detection algorithms in mobile view,a real-time shadow detection algorithm based on track and detection framework was proposed.Firstly,the overlapped shadow parts of the two frames were tracked twice,the tracking points with larger error were filtered by forward and backward tracking,and the accuracy of the tracking edge was ensured by Canny edge confidence.Then,the new region to be detected was obtained by the region division method based on optical flow.Secondly,seven-dimensional feature vectors were constructed for texture edge error detection,soft shadow detection,and dark area error detection.Then the support vector machine(SVM)classifier was trained by extracting feature vectors from shadow edge,and the trained classifier was employed to detect the shadow in the new area.Finally,for the broken edges in the detection results,an algorithm based on RGB color space gradient direction consistency was proposed to optimize the connection of the broken edge.Experimental results show that the proposed algorithm exhibits the best comprehensive performance compared with the latest research results,and is superior to the existing methods in terms of detection accuracy and edge continuity.