Effective Observation Of Random Fields PDF Download

To act intelligently in presence of others, robots must use information from their sensors to recognize behaviors and activities of other agents in their environment. We explore how to bridge the gap from noisy, continuous observations about the world to high-level, discrete activity labels. We contribute the use of conditional random fields (CRFs) for activity recognition in multirobot domains. We explore appropriateness of CRFs with an empirical comparison to hidden Markov models. We elucidate the properties of CRFs that make them well suited to the activity recognition, namely discriminative training, the ability to robustly incorporate rich features of observations, and their nature as conditional models, with a variety of synthetic and real robot data. Accurate activity recognition requires complex and rich features of the observations. We choose the most informative features from a large set of candidates using feature selection. We adapt two feature selection algorithms, grafting and '1 regularization, to conditional random fields. We also investigate a third feature selection algorithm, which was originally proposed for CRFs in a natural language processing domain, in an activity recognition context. In particular, we focus on scaling feature selection to very large sets of candidate features that we define succinctly using a rich relational feature specification language. The reduced feature sets that we discover via feature selection enable efficient, real-time inference. However, feature selection and training for conditional random fields is computationally expensive. We adapt an M-estimator, introduced by Jeon and Lin for log-density estimation in ANOVA models, for fast, approximate parameter estimation in CRFs. We provided an in depth, empirical evaluation of the properties of the M-estimator and then we introduce a new, efficient feature selection algorithm for CRFs based around M-estimation to identify the most important features.