Real-time Learning and Detection of 3D Texture-less Objects: A Scalable Approach
We have developed a scalable method for detecting multiple objects from a video stream in real time. The method is shape-based, thus suitable for texture-less objects. The method is based on constellations of edgelets, which are easy to calculate and occlusion-tolerant. Scalability is handled by fixed scanning paths that limit the number of considered constellations. Searching training views for constellations using a few fixed scanning paths builds a library of transformation-invariant descriptors. During testing, the image is searched for constalltions of edgelets using the same pre-defined fixed scanning paths. When a constellation is found, the descriptor is compared to the library to find candidate matche. The method was tested for up to 30 three-dimensional objects (> 100 views per object) and recall of over 50% was achieved at 7fps.
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Publications
Real-time Learning and Detection of 3D Texture-less Objects: A Scalable Approach. British Machine Vision Conference (BMVC), 2012, pdf, abstract
Videos
Real-time and scalable detection of textureless objects using monocular camera AVI (13.8MB)
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