| ROBUST VIDEO-BASED RECOGNITION OF DYNAMIC HEAD GESTURES IN VARIOUS DOMAINS- COMPARING A RULE-BASED AND A STOCHASTIC APPROACH (2008) | |||||||||||||||
Abstract | |||||||||||||||
| This work describes two video-based approaches for detecting and classifying dynamic head-gestures. We compare a simple, fast, and efficient rule-based algorithm with a powerful, robust, and flexible stochastic implementation. In both realizations, the head is localized via a combination of color- and shape-based segmentation. For a continuous feature extraction, the rule-based approach uses a templatematching of the nose bridge. In addition, the stochastic algorithm applies features derived from the optical flow, and classifies them by a set of discrete Hidden Markov Models. The rule-based implementation evaluates the key-feature in a finite state machine. We extensively tested the systems in two different application domains (VR-desktop scenario vs. automotive environment). Six different gestures can be classified with an overall recognition rate of 93.7 % (rule-based) and 97.3 % (stochastic) in the VR (92.6 % and 95.5 % in the automotive environment, respectively). Both approaches work independently from the image background. Concerning the stochastic concept, further gesture types can easily be implemented. The modules were designed as part of a multimodal system architecture for an increase of interaction comfort and intuitive handling. 1. | |||||||||||||||
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