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Benchmarking Qualitative Spatial Calculi for Video Activity Analysis
| Citations: | 5 - 2 self |
BibTeX
@MISC{Sridhar_benchmarkingqualitative,
author = {Muralikrishna Sridhar and Anthony G Cohn and David C Hogg},
title = {Benchmarking Qualitative Spatial Calculi for Video Activity Analysis},
year = {}
}
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Abstract
Abstract. This paper presents a general way of addressing problems in video activity understanding using graph based relational learning. Video activities are described using relational spatio-temporal graphs, that represent qualitative spatiotemporal relations between interacting objects. A wide range of spatio-temporal relations are introduced, as being well suited for describing video activities. Then, a formulation is proposed, in which standard problems in video activity understanding such as event detection, are naturally mapped to problems in graph based relational learning. Experiments on video understanding tasks, for a video dataset consisting of common outdoor verbs, validate the significance of the proposed approach. 1
Keyphrases
video activity video activity analysis qualitative spatial calculus relational learning relational spatio-temporal graph video dataset consisting qualitative spatiotemporal relation general way spatio-temporal relation standard problem event detection wide range common outdoor verb video understanding task
