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Conference Paper

Hough-based Object Detection with Grouped Features


Srikantha,  Abhilash
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Srikantha, A., & Gall, J. (2014). Hough-based Object Detection with Grouped Features. In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 1653-1657). IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0024-E3A9-C
Hough-based voting approaches have been successfully applied to object detection. While these methods can be efficiently implemented by random forests, they estimate the probability for an object hypothesis for each feature independently. In this work, we address this problem by grouping features in a local neighborhood to obtain a better estimate of the probability. To this end, we propose oblique classification-regression forests that combine features of different trees. We further investigate the benefit of combining independent and grouped features and evaluate the approach on RGB and RGB-D datasets.