Behaviour Recognition for Spatially Unconstrained Unmanned Vehicles

We use trajectory based techniques to perform location independent behaviour recognition on an unmanned underwater vehicle. Unmanned vehicles have applications in both surveillance and structural inspection, but require robust, location independent behaviour recognition. Previous research has used GPS or location based states that tie model parameters to specific locations, and have rarely considered performance under specific levels of noise. We use location independent action based states to recognise high level behaviour using flat Hidden Markov Models. We validate this approach by comparing performance under different levels of noise, achieving 78% classification precision under 50% corruption.

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