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
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A model of dynamic visual attention for object tracking in natural image sequences
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Autonomous behavior-based switched top-down and bottom-up visual attention for mobile robots
IEEE Transactions on Robotics
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In this paper, we introduce a cognitive approach for object tracking from a mobile platform. The approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on concepts of the human visual system. A top-down guided visual search module of the system enables to especially favor features which fit to a previously learned target object. Here, the appearance of an object is learned online within the first image in which it is detected. In subsequent images, the attention system searches for the target features and builds a top-down, target-related saliency map. This enables to focus on the most relevant features of especially this object in especially this scene without knowing anything about a particular object model or scene in advance. The system is able to operate in real-time and to cope with the requirements of real-world tasks such as illumination variations and other moving objects.