Video key frame extraction through dynamic Delaunay clustering with a structural constraint

Key frame based video summarization has emerged as an important area of research for the multimedia community. Video key frames enable an user to access any video in a friendly and meaningful way. In this paper, we propose an automated method of video key frame extraction using dynamic Delaunay graph clustering via an iterative edge pruning strategy. A structural constraint in form of a lower limit on the deviation ratio of the graph vertices further improves the video summary. We also employ an information-theoretic pre-sampling where significant valleys in the mutual information profile of the successive frames in a video are used to capture more informative frames. Various video key frame visualization techniques for efficient video browsing and navigation purposes are incorporated. A comprehensive evaluation on 100 videos from the Open Video and YouTube databases using both objective and subjective measures demonstrate the superiority of our key frame extraction method.     

基于混合高斯模型和帧差的火焰前景提取算法

随着计算机视觉技术和模式识别技术的进一步发展,利用图像处理技术识别火焰引起了人们的重视,文中将帧差法和混合高斯模型法相结合,提出了一种新型火焰前景提取算法。接下来根据火焰的静态特征和动态特征,通过火焰频率特征、相关性特征、圆形度特征、色彩特征等,能准确识别出几种不同室内环境下突发的火焰,同时能有效地避免光线移动、人影晃动所造成的干扰,实验结果表明,此算法的识别准确率在90%以上,识别时间大约在5s~20s。     

Visual video evaluation association modeling based on chaotic pseudo-random multi-layer compressed sensing for visual privacy-protected keyframe extraction

In current society, artificial intelligence processing technology offers convenient video monitoring, but also raises the risk of privacy leakage. Theoretically, the data used in intelligent video processing methods may directly convey visual information containing private content. For the above problem, this paper uses a multi-layer visual privacy-protected (VPP) coding method to blur private content in the video at the visual level, while avoiding the loss of important visual features contained in the video as much as possible. And this provides a guarantee of the quality of the subsequent keyframe extraction step. Then a visual evaluation algorithm is proposed for assessing the quality of VPP-encoded video privacy protection. And the experiment shows that the results are consistent with those of subjective evaluation. In addition, for VPP-encoded video, we propose an unsupervised two-layer clustering keyframe extraction method with corresponding performance evaluation index. Finally, an association model is established to balance the privacy protection quality and the keyframe extraction performance.