Object Detection using Geometrical Context Feedback

  • Authors:

  • Min Sun;Sid Yingze Bao;Silvio Savarese

  • Affiliations:

  • University of Michigan, Ann Arbor, USA;University of Michigan, Ann Arbor, USA;University of Michigan, Ann Arbor, USA

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

We propose a new coherent framework for joint object detection, 3D layout estimation, and object supporting region segmentation from a single image. Our approach is based on the mutual interactions among three novel modules: (i) object detector; (ii) scene 3D layout estimator; (iii) object supporting region segmenter. The interactions between such modules capture the contextual geometrical relationship between objects, the physical space including these objects, and the observer. An important property of our algorithm is that the object detector module is capable of adaptively changing its confidence in establishing whether a certain region of interest contains an object (or not) as new evidence is gathered about the scene layout. This enables an iterative estimation procedure where the detector becomes more and more accurate as additional evidence about a specific scene becomes available. Extensive quantitative and qualitative experiments are conducted on the table-top dataset (Sun et al. in ECCV, 2010b) and two publicly available datasets (Hoiem et al. in CVPR, 2006; Sudderth et al. in IJCV, 2008), and demonstrate competitive object detection, 3D layout estimation, and segmentation results.