Analyzing Team Actions with Cascading HMM

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 subteams 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 unaccepta...

• Type: Journal
• Year: 2009
• Where: FLAIRS
• Authors: Brandyn Allen White, Nate Blaylock, Ladislau Bölöni
• Keywords: Artificial Intelligence, Bounding Overwatch, CHMM, FLAIRS 2009, Team Action