Learning structured Hough voting for joint object detection and occlusion estimation
Tao Wang (NICTA & ANU)
COMPUTER VISION AND ROBOTICS SERIESDATE: 2013-05-30
TIME: 16:00:00 - 17:00:00
LOCATION: NICTA - 7 London Circuit
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ABSTRACT:
We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments. First, we extend the Hough hypothesis space to include both object location and its visibility pattern, and design a new score function that accumulates votes for object detection and occlusion prediction. In addition, we explore the correlation between objects and their environment, building a depth-encoded object-context model based on RGB-D data. Particularly, we design a layered context representation and allow image patches from both objects and backgrounds voting for the object hypotheses. We demonstrate that using a data-driven 2.1D representation we can learn visual codebooks with better quality, and more interpretable detection results in terms of spatial relationship between objects and viewer. We test our algorithm on two challenging RGB-D datasets with signiicant occlusion and intraclass variation, and demonstrate the superior performance of our method.
BIO:
Tao Wang is a PhD student with NICTA Canberra Research Laboratory and College of Engineering and Computer Science, Australian National University. His research interests include computer vision and machine learning. Recently he has been working on projects for object localization and indoor scene understanding.
