Abstract
Biologically motivated attention systems prefilter the visual environment for scene elements that pop out most or match the current system task best. However, the robustness of biological attention systems is difficult to achieve, given e.g., the high variability of scene content, changes in illumination, and scene dynamics. Most computational attention models do not show real time capability or are tested in a controlled indoor environment only. No approach is so far used in the highly dynamic real world scenario car domain. Dealing with such scenarios requires a strong system adaptation capability with respect to changes in the environment. Here, we focus on five conceptual issues crucial for closing the gap between artificial and natural attention systems operating in the real world. We show the feasibility of our approach on vision data from the car domain. The described attention system is part of a biologically motivated advanced driver assistance system running in real time.
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Michalke, T., Fritsch, J., Goerick, C. (2008). Enhancing Robustness of a Saliency-Based Attention System for Driver Assistance. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_5
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DOI: https://doi.org/10.1007/978-3-540-79547-6_5
Publisher Name: Springer, Berlin, Heidelberg
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