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In this paper, we introduce a first-order probabilistic model that combines multiple cues to classify human activities from video data accurately and robustly. Our system works in a realistic office setting with background clutter, natural illumination, different people, and partial occlusion. The model we present is compact, requires only fifteen sentences of first-order logic grouped as a Dynamic Markov Logic Network (DMLNs) to implement the probabilistic model and leverages existing state-of-the-art work in pose detection and object recognition.

Abstract-Sensor networks present the opportunity to accurately localize phenomena of interesi. To be able to do so however, sensor nodes, need to themselves be accurately localized. We present an algorithm to do this based on uncontrolled environmental sounds observed by each of the sensor nodes. A probabilistic generative model is presented and it is shown that the sensor node localization problem is equivalent to maximum likelihood estimation in the model. Experimental results are presented for both simulated sensor nodes and Crossbow MICA2 sensor nodes.

We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques. This capability is useful in that it allows sensor networks to answer queries effectively even when present information is partially corrupt and when more information is unavailable or too costly to obtain. We use a Bayesian network to model the sensor network and Markov Chain Monte Carlo sampling to perform approximate inference. We demonstrate our technique on the specific problem of determining whether a friendly agent is surrounded by enemy agents and present simulation results for it.

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