Abstract

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.

Keywords:
Term (time) Computer science Benchmark (surveying) Representation (politics) Set (abstract data type) Activity recognition Event (particle physics) Artificial intelligence Event calculus Machine learning Programming language

Metrics

53
Cited By
6.81
FWCI (Field Weighted Citation Impact)
20
Refs
0.97
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Logic, Reasoning, and Knowledge
Physical Sciences →  Computer Science →  Artificial Intelligence
Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Semantic Web and Ontologies
Physical Sciences →  Computer Science →  Artificial Intelligence

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