基于SIFT特征点的视频镜头突变检测改进算法

Improved algorithm for shot mutation detection based on SIFT feature points

该文提出了一种改进的结合SIFT特征点提取的视频镜头突变检测算法。针对SIFT算法特征描述子维数过高的问题,该文在SIFT算法基础上重新划分特征点邻域,将特征描述子维数降低50%。实验结果表明,改进的SIFT算法视频镜头突变检测平均查全率达到了97. 84%,查准率达到了96. 83%,比文献值分别高出2. 05%和2. 38%,平均每秒完成特征点提取的视频帧数为42. 4187,每一秒的特征点提取效率提高了60. 49%。提高了镜头变化检测的精度和时间效率。

In the paper,an improved video shot mutation detection algorithm based on SIFT feature extraction is proposed.Aiming at the problem that the sub-dimension of SIFT algorithm is too high,this paper re-divides the feature point neighborhood based on SIFT algorithm and reduces the feature descriptor sub-dimension by 50%.The experimental results show that the average recall rate of the video shot mutation detection of the improved SIFT algorithm reaches 97.84%,and the precision rate reaches 96.83%,which is 2.05%and 2.38%higher than the literature value respectively.The number of video frames extracted by the point is 42.4187,and the feature point extraction efficiency per second is improved by 60.49%,improving the accuracy and time efficiency of lens change detection.