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
A Goal Oriented Attention Guidance Model
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Robust real-time lane and road detection in critical shadow conditions
ISCV '95 Proceedings of the International Symposium on Computer Vision
Attending to Visual Motion: Localizing and Classifying Affine Motion Patterns
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Color object recognition in real-world scenes
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Visual search in static and dynamic scenes using fine-grain top-down visual attention
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Enhancing robustness of a saliency-based attention system for driver assistance
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Non-Gaussian velocity distributions integrated over space, time, and scales
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The use of computer vision for assisting the driver dates back to first research projects in 1980s, but only recently the progress in vision research and the increase in computational power have resulted in actual products. Although impressive from the robustness point of view, these systems are optimized for specific problems and at best perform reactive tasks like, e.g., lane keeping assistance. However, for a better understanding of generic traffic situations and for assisting the driver in the full range of his actions, integrated and more flexible approaches are needed. In this contribution we propose a vision system that in important aspects is inspired by the human visual system for organizing the different visual routines that need to be carried out. The presented system searches for biological motivation in case classical engineering-based approaches cannot do better or fail. Using a tunable visual attention system and state-of-the-art perception algorithms, the system is capable of analyzing the scenery for task-relevant information in order to provide the driver with assistance in dangerous situations. Our main research focus is on the design of general mechanisms (i.e., not domain or task-specific) that lead to a certain observable behavior without being explicitly designed for this behavior. Using this principle, we aim at developing easily extensible driver assistance systems. The system components are evaluated on a complex inner-city scene and on further real-world data. We demonstrate the performance of the integrated vision system in a construction site setup. A traffic jam within the construction site results in a dangerous situation that the system has to identify in order to warn the driver. Different from other systems the detection of the dangerous situation is based on the vision channel alone. Radar is only used to assign distance data to visually detected objects. The contribution represents an important intermediate stage for future, more cognitive driver assistance systems.