A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Robotics and Autonomous Systems
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Enhancing robustness of a saliency-based attention system for driver assistance
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Non-Gaussian velocity distributions integrated over space, time, and scales
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we propose a system architecture that extends the current state-of-the-art in computational visual attention by incorporating the biological concept of ventral attention. According to recent findings regarding the neurobiological foundations of attention, there exist two separate but interacting attention systems in the human brain: the dorsal attention system and the ventral attention system. As opposed to the well-known computational concepts of bottom-up and top-down saliency, which both correspond to the dorsal attention system, the ventral attention system is sensitive to behavior-relevant stimuli that are unexpected (i.e. not top-down salient), independent of their perceptual saliency (bottom-up saliency). This results in a dynamic interplay between top-down saliency, bottom-up saliency and ventral attention in the proposed system architecture, enabling the system to redirect its focus of attention to important stimuli while being absorbed in a task, even if their perceptual saliency is low. Our technical system instance implementing the proposed architecture integrates several state-of-the-art methods in a coherent system and concentrates on unexpected motion as a first technical account of ventral attention. In our experiments, we demonstrate that the ventral attention enables our system to detect and reorient to important situations in real-world traffic environments that are relevant for the behavior of driving.