Title

Analyzing Team Actions With Cascading Hmm

Abstract

While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions. This includes recognizing the current state of the action, predicting the next state, recognizing deviations from the standard action model, and handling ambiguous cases. The underlying probabilistic reasoning model has a major impact on the type of data it can extract, its accuracy, and the computational cost of the reasoning process. In this paper we are using Cascading Hidden Markov Models (CHMM) to analyze Bounding Overwatch, an important team action in military tactics. The team action is represented in the CHMM as a plan tree. Starting from real-world recorded data, we identify the sub- teams through clustering and extract team oriented discrete features. In an experimental study, we investigate whether the better scalability and the more structured information provided by the CHMM comes with an unacceptable cost in accuracy. We find that a properly parametrized CHMM estimating the current goal chain of the Bounding Overwatch plan tree comes very close to a flat HMM estimating only the overall Bounding Overwatch state (a subset of the goal chain) at a respective overall state accuracy of 95% vs 98%, making the CHMM a good candidate for deployed systems. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved.

Publication Date

11-4-2009

Publication Title

Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22

Number of Pages

129-134

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

70350453566 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70350453566

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