Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
A dynamic attention system that reorients to unexpected motion in real-world traffic environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
Efficient neural models for visual attention
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
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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.