摘要翻译：
我们一直在开发一个识别人类活动的系统，给出视频内容的符号表示。我们系统的输入是在视频帧上检测到的一组带有时间戳的短期活动。我们系统的输出是一组公认的长期活动，它们是短期活动的预定义时间组合。对短期活动的约束，如果满足，将导致对长期活动的识别，使用事件演算的方言来表达。我们通过几个典型的复杂活动的表现来说明方言的表现力。在此基础上，通过对监控视频基准数据集的实验，对系统进行了详细的评价。
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英文标题：
《A Logic Programming Approach to Activity Recognition》
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作者：
A. Artikis, M. Sergot and G. Paliouras
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最新提交年份：
2013
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We have been developing a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities detected on video frames. The output of our system is a set of recognised long-term activities, which are pre-defined temporal combinations of short-term activities. The constraints on the short-term activities that, if satisfied, lead to the recognition of a long-term activity, are expressed using a dialect of the Event Calculus. We illustrate the expressiveness of the dialect by showing the representation of several typical complex activities. Furthermore, we present a detailed evaluation of the system through experimentation on a benchmark dataset of surveillance videos.
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PDF链接：
https://arxiv.org/pdf/0905.4614