We present 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 (STA) detected on video frames. The output
is a set of recognised long-term activities (LTA), which are pre-defined
temporal combinations of STA. The constraints on the STA that, if satisfied,
lead to the recognition of a LTA, have been expressed using a dialect of the
Event Calculus. In order to handle the uncertainty that naturally occurs in
human activity recognition, we adapted this dialect to a state-of-the-art
probabilistic logic programming framework. We present a detailed evaluation and
comparison of the crisp and probabilistic approaches through experimentation on
a benchmark dataset of human surveillance videos.

基于符号表示的视频内容，我们提出了一个识别人类活动的系统：以时间戳的短期活动 (STA) 为输入，输出由预定义的 STA 时间组合的长期活动 (LTA)，这些 STA 限制已经通过事件演算的方言表达，并适应了最新的概率逻辑编程框架，通过对人类监控视频的基准数据集的实验来进行详细评估和比较。

概率逻辑编程事件演算

A Probabilistic Logic Programming Event Calculus

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.

使用事件演算法识别基于视频内容的短期活动，进而推断长期预定义活动的系统及其详细评估结果。