Salehin Md Musfequs, Paul Manoranjan, Kabir Muhammad Ashad
School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW-2795, Australia.
PLoS One. 2017 Nov 9;12(11):e0181636. doi: 10.1371/journal.pone.0181636. eCollection 2017.
Video summarization is a process to extract objects and their activities from a video and represent them in a condensed form. Existing methods for video summarization fail to detect moving (dynamic) objects in the low color contrast area of a video frame due to the pixel intensities of objects and non-objects are almost similar. However, edges of objects are prominent in the low contrast regions. Moreover, to represent objects, geometric primitives (such as lines, arcs) are distinguishable and high level shape descriptors than edges. In this paper, a novel method is proposed for video summarization using geometric primitives such as conic parts, line segments and angles. Using these features, objects are extracted from each video frame. A cost function is applied to measure the dissimilarity of locations of geometric primitives to detect the movement of objects between consecutive frames. The total distance of object movement is calculated and each video frame is assigned a probability score. Finally, a set of key frames is selected based on the probability scores as per user provided skimming ratio or system default skimming ratio. The proposed approach is evaluated using three benchmark datasets-BL-7F, Office, and Lobby. The experimental results show that our approach outperforms the state-of-the-art method in terms of accuracy.
视频摘要提取是一个从视频中提取对象及其活动并以浓缩形式呈现它们的过程。现有的视频摘要提取方法由于对象和非对象的像素强度几乎相似，无法在视频帧的低颜色对比度区域检测到移动（动态）对象。然而，对象的边缘在低对比度区域中很突出。此外，为了表示对象，几何基元（如直线、弧线）比边缘更具辨识度且是更高层次的形状描述符。本文提出了一种使用圆锥部分、线段和角度等几何基元进行视频摘要提取的新方法。利用这些特征，从每个视频帧中提取对象。应用一个代价函数来衡量几何基元位置的差异，以检测连续帧之间对象的移动。计算对象移动的总距离，并为每个视频帧分配一个概率分数。最后，根据用户提供的浏览比率或系统默认浏览比率，基于概率分数选择一组关键帧。使用三个基准数据集——BL - 7F、办公室和大厅对所提出的方法进行评估。实验结果表明，我们的方法在准确性方面优于现有方法。
