A computational framework for attentional object discovery in RGB-D videos.
We present a computational framework for attention-guided visual scene exploration in sequences of RGB-D data. For this, we propose a visual object candidate generation method to produce object hypotheses about the objects in the scene. An attention system is used to prioritise the processing of visual information by (1) localising candidate objects, and (2) integrating an inhibition of return (IOR) mechanism grounded in spatial coordinates. This spatial IOR mechanism naturally copes with camera motions and inhibits objects that have already been the target of attention. Our approach provides object candidates which can be processed by higher cognitive modules such as object recognition. Since objects are basic elements for many higher level tasks, our architecture can be used as a first layer in any cognitive system that aims at interpreting a stream of images. We show in the evaluation how our framework finds most of the objects in challenging real-world scenes.
Institute of Computer Science VI, University of Bonn, Bonn, Germany. martin@ais.uni-bonn.de.
Germán
G
Martín García
Institute of Computer Science VI, University of Bonn, Bonn, Germany.
Mircea
M
Pavel
Computer Vision Group, Department of Informatics, University of Hamburg, Hamburg, Germany.
Simone
S
Frintrop
D000465:Algorithms; D001288:Attention / Q000502:physiology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007266:Inhibition, Psychological; D008297:Male; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D017603:Signal Detection, Psychological / Q000502:physiology*; D013997:Time Factors; D014741:Video Recording / Q000379:methods
3D inhibition of return; Computational visual attention; RGB-D object discovery