基于动态阈值的视频场景切换检测

视频场景切换检测在视频处理领域十分重要,优秀的视频场景切换检测算法对于视频的后续处理,包括信息标注与特征提取等都具有很大的意义.本文以传统的图像处理方法为基础,结合ORB (oriented FAST and rotated BRIEF)快速特征描述子,分别实现图像的相似度估计.通过动态融合因子,将多种基本算法得到的相似度在完整算法中的价值量化,并根据邻帧相似度计算动态阈值,从而提高了算法的稳定性;另外通过流程规划,减少了算法中耗时长的部分的调用次数,最终实现了高效的视频场景切换检测.实验结果表明,视频场景切换检测的准确率有一定程度地提升.

Video Scene Switching Detection Based on Dynamic Threshold

Video scene switching detection is important in video processing. An excellent algorithm for video scene switching detection is of great significance for subsequent video processing, including information annotation and feature extraction. In this study, on the basis of the traditional image processing methods and oriented FAST and rotated BRIEF (ORB), the similarity estimation of images is realized separately. Through the dynamic fusion factor, the value of similarity obtained by various basic algorithms in the complete algorithm is quantified, and the dynamic threshold is calculated according to the similarity of neighboring frames to improve the stability of the algorithm. In addition, by flow planning, the number of calls of the time-consuming part of the algorithm is reduced, and efficient video scene switching detection is finally realized. The experimental results show that the accuracy of video scene switching detection is improved to a certain extent.