IEEE Trans Image Process. 2018 Aug;27(8):3798-3812. doi: 10.1109/TIP.2018.2823420.
Video synopsis is an effective technique for surveillance video browsing and storage. However, most of the existing video synopsis approaches are not suitable for complex situations, especially crowded scenes. This is because these approaches heavily depend on the preprocessing results of foreground segmentation and multiple objects tracking, but the preprocessing techniques usually achieve poor performance in crowded scenes. To address this problem, we propose a comprehensive video synopsis approach which can be applied to scenes with drastically varying crowdedness. The proposed approach differs significantly from the existing methods, and has several appealing properties. First, we propose to detect the crowdedness of a given video, then, extract object tubes in sparse periods and extract video clips in crowded periods, respectively. Through such a solution, the poor performance of preprocessing techniques in crowded scenes can be avoided by extracting the whole video frames. Second, we propose a group-partition algorithm which can discovers the relationships among moving objects and alleviates several segmentation and tracking errors. Third, a group-based greedy optimization algorithm is proposed to automatically determine the length of a synopsis video. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach.
视频摘要技术是一种有效的监控视频浏览和存储技术。然而，大多数现有的视频摘要方法都不适用于复杂的情况，尤其是拥挤的场景。这是因为这些方法严重依赖于前景分割和多个目标跟踪的预处理结果，但是这些预处理技术在拥挤的场景中通常表现不佳。为了解决这个问题，我们提出了一种全面的视频摘要方法，该方法适用于拥挤程度变化很大的场景。所提出的方法与现有的方法有很大的不同，具有几个吸引人的特性。首先，我们提出检测给定视频的拥挤度，然后，在稀疏时段提取对象管，在拥挤时段提取视频剪辑。通过这样的解决方案，可以避免预处理技术在拥挤场景中表现不佳的问题，从而提取整个视频帧。其次，我们提出了一种分组分区算法，可以发现运动物体之间的关系，并减轻一些分割和跟踪错误。第三，提出了一种基于组的贪婪优化算法，用于自动确定摘要视频的长度。此外，我们进行了广泛的实验，证明了所提出方法的有效性和效率。
