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Improving object detection using 3D spatial relationships

University of British Columbia

Reliable object detection is one of the most significant hurdles that must be overcome to develop useful household robots. Overall, the goal of this work is to demonstrate how effective 3D qualitative spatial relationships can be for improving object detection. We show that robots can utilize 3D qualitative spatial relationships to improve object detection by differentiating between true and false positive detections. The main body of the thesis focuses on an approach for improving object detection rates that identifies the most likely subset of 3D detections using seven types of 3D relationships and adjusts detection confidence scores to improve the average precision. These seven 3D qualitative spatial relationships are adapted from 2D qualitative spatial reasoning techniques. We learn a model for identifying the most likely subset using a structured support vector machine [Tsochantaridis et al., 2004] from examples of 3D layouts of objects in offices and kitchens. We produce 3D detections from 2D detections using a fiducial marker and images of a scene and show our model is successful at significantly improving overall detection rates on real world scenes of both offices and kitchens. After the real world results, we test our method on synthetic detections where the properties of the 3D detections are controlled. Our approach improves on the model it was based upon, that of [Desai et al., 2009], by utilizing a branch and bound tree search to improve both training and inference. Our model relies on sufficient true positive detections in the training data or good localization of the true positive detections. Finally, we analyze the cumulative benefits of the spatial relationships and determine that the most effective spatial relationships depend on both the scene type and localization accuracy. We demonstrate that there is no one relationship that is sufficient on its own or always outperforms others and that a mixture of relationships is always useful.

Text

2013-08-13

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/44786

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/