Department of Computer Science, Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA.
IEEE Trans Pattern Anal Mach Intell. 2010 Dec;32(12):2246-61. doi: 10.1109/TPAMI.2010.33.
There is now a growing need to identify various kinds of activities that occur in videos. In this paper, we first present a logical language called Probabilistic Activity Description Language (PADL) in which users can specify activities of interest. We then develop a probabilistic framework which assigns to any subvideo of a given video sequence a probability that the subvideo contains the given activity, and we finally develop two fast algorithms to detect activities within this framework. OffPad finds all minimal segments of a video that contain a given activity with a probability exceeding a given threshold. In contrast, the OnPad algorithm examines a video during playout (rather than afterwards as OffPad does) and computes the probability that a given activity is occurring (even if the activity is only partially complete). Our prototype Probabilistic Activity Detection System (PADS) implements the framework and the two algorithms, building on top of existing image processing algorithms. We have conducted detailed experiments and compared our approach to four different approaches presented in the literature. We show that-for complex activity definitions-our approach outperforms all the other approaches.
现在越来越需要识别视频中发生的各种活动。在本文中，我们首先提出了一种称为概率活动描述语言（PADL）的逻辑语言，用户可以使用该语言指定感兴趣的活动。然后，我们开发了一个概率框架，该框架为给定视频序列的任何子视频分配一个包含给定活动的子视频的概率，最后我们开发了两个在该框架内检测活动的快速算法。OffPad 找到包含给定活动的视频的所有最小片段，其概率超过给定阈值。相比之下，OnPad 算法在播放期间检查视频（而不是像 OffPad 那样在之后检查），并计算给定活动正在发生的概率（即使活动只是部分完成）。我们的概率活动检测系统（PADS）原型实现了该框架和两个算法，构建在现有的图像处理算法之上。我们进行了详细的实验，并将我们的方法与文献中提出的四种不同方法进行了比较。我们表明，对于复杂的活动定义，我们的方法优于所有其他方法。
