H.264压缩域中利用Biased-SVM检测镜头边界

摘要

为了直接从H.264码流中检测镜头边界，提出了利用H.264压缩域多特征和Biased-SVM（不平衡支持向量机）分类算法的检测方法。分析帧类型、宏块类型、运动矢量、帧内预测模式等信息，以获得发生镜头突变和渐变的特征。针对镜头边界帧的数量远少于视频帧总数的特点，用Biased-SVM分类方法将视频帧分为突变帧、渐变帧和非镜头边界帧。在TRECVID视频集上的实验结果表明，与其他H.264压缩域的算法相比，该算法有更好的性能。%In order to detect shot boundaries in H.264 bit streams, a shot boundary detection method using compressed domain features of H.264 and Biased-SVM(Biased Support Vector Machine)is proposed. The features about the abrupt shot changes and gradual shot changes are obtained by analyzing the information of frame type, macroblock type, motion vector, intra-prediction mode, etc. As the number of shot boundary frames is far fewer than the total number of video frames, proposed method chooses Biased-SVM to classify the frames into three classes, namely, the frames of abrupt change, gradual change and non-change. Experi-mental results on TRECVID video dataset indicate that the presented approach has better performance on shot boundary detection, compared with other method in H.264 compressed domain.