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System Approach for Multi-Purpose Representations of Traffic Scene Elements
"... Abstract — A major step towards intelligent vehicles lies in the acquisition of an environmental representation of sufficient generality to serve as the basis for a multitude of different assistance-relevant tasks. This acquisition process must reliably cope with the variety of environmental changes ..."
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Abstract — A major step towards intelligent vehicles lies in the acquisition of an environmental representation of sufficient generality to serve as the basis for a multitude of different assistance-relevant tasks. This acquisition process must reliably cope with the variety of environmental changes inherent to traffic environments. As a step towards this goal, we present our most recent integrated system performing object detection in challenging environments (e.g., inner-city or heavy rain). The system integrates unspecific and vehicle-specific methods for the detection of traffic scene elements, thus creating multiple object hypotheses. Each detection method is modulated by optimized models of typical scene context features which are used to enhance and suppress hypotheses. A multi-object tracking and fusion process is applied to make the produced hypotheses spatially and temporally coherent. In extensive evaluations we show that the presented system successfully analyzes scene elements under diverse conditions, including challenging weather and changing scenarios. We demonstrate that the used generic hypothesis representations allow successful application to a variety of tasks including object detection, movement estimation, and risk assessment by time-to-contact evaluation. I.
in an intelligent car
"... The contribution of context information: a case study of object recognition ..."
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The contribution of context information: a case study of object recognition
Language Acquisition Embedded into Tutor-Robot Interaction
"... Children acquire language to a large extend in the interaction with their caregivers. Inspired by this observation we develop computational models and artifacts for the acquisition of language in an interactive scenario. The artifact bootstraps its representations with little a priori knowledge and ..."
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Children acquire language to a large extend in the interaction with their caregivers. Inspired by this observation we develop computational models and artifacts for the acquisition of language in an interactive scenario. The artifact bootstraps its representations with little a priori knowledge and can be taught by a human tutor. In this framework we investigate different aspects of the speech acquisition process. This encompasses the learning of speech features, word and sub-word units as well as the production of acquired speech units. As speech features we explore a set of hierarchical spectro-temporal features which are learned in an unsupervised fashion based on the observed speech data. Phone-like speech units emerge from an unsupervised clustering process. These phone-units can then be used to bootstrap word learning in an interactive scenario where a tutor shows a visual property and at the same time utters a corresponding speech label. Thereby an auditory attention system and predefined key phrases trigger the learning behavior. Finally the learned units can also be reproduced. Index Terms: speech acquisition, speech features, word learning, speech synthesis, attention system
