NDLI: A Markov logic framework for recognizing complex events from multimodal data

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A Markov logic framework for recognizing complex events from multimodal data

Content Provider	ACM Digital Library
Author	Kautz, Henry Song, Young Chol Li, Yuncheng Luo, Jiebo Zhang, Ce Swift, Mary Allen, James
Abstract	We present a general framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including objects and gestures recognized by RGB-D and descriptions of events extracted from recognized and parsed speech. The system outputs a complete reconstruction of the agent's plan, explaining actions in terms of more complex activities and filling in unobserved but necessary events. We show how to use Markov Logic (a probabilistic extension of first-order logic) to create a model in which observations can be partial, noisy, and refer to future or temporally ambiguous events; complex events are composed from simpler events in a manner that exposes their structure for inference and learning; and uncertainty is handled in a sound probabilistic manner. We demonstrate the effectiveness of the approach for tracking kitchen activities in the presence of noisy and incomplete observations.
Starting Page	141
Ending Page	148
Page Count	8
File Format	PDF
ISBN	9781450321297
DOI	10.1145/2522848.2522883
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-12-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Plan recognition Markov logic Multimodal interaction
Content Type	Text
Resource Type	Article

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