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Multi-view Manhole Detection, Recognition and 3D Localisation

Multi-view Manhole Detection, Recognition and 3D Localisation. Radu Timofte and Luc Van Gool. Problem definition. Input: Large set of views and corresponding camera locations Output: List of manholes. Manholes. High variance in manhole patterns around the world .

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Multi-view Manhole Detection, Recognition and 3D Localisation

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  1. Multi-view Manhole Detection, Recognition and 3D Localisation Radu Timofte and Luc Van Gool

  2. Problem definition • Input: Large set of views and corresponding camera locations • Output: List of manholes

  3. Manholes • High variance in manhole patterns around the world . • We DO use texture models for manhole validation. For each new region, new texture models are to be trained.

  4. Outline Single view • Segmentation – fast segment selection process with very few missed manholes. • Manholes are usually distinguishable from the surrounding environment => have distinctive textures, shapes, symmetry. • Mean shift method is employed for color segmentation. • Detection – Classifiers based on histograms of Local Binary Patterns as texture descriptors. Multi-view • Global optimization – over single-view detections constrained by 3D geometry

  5. Original image Ground plane projection Segmented image → → Edge Detection and Image Segmentation • The image is projected on the estimated ground plane. • Edge detection and mean shift1 in L*u*v* color space are combined for segmentation 1 D.Comaniciu, P.Meer, “Mean shift: A robust approach toward feature space analysis”, PAMI, 2002

  6. Segmented image Texture image Radial symmetry Projected image + + = Detection • Local Binary Patterns2 is used as a texture descriptor model and radial symmetry3 has pruning purposes. • Each segment is classified according its LBP histogram as manhole or background. 2T.Ojala et al, “Multiresolution gray-scale and rotation invariant texture classication with Local Binary Patterns”, PAMI, 2002 3G.Loy and A. Zelinsky, “Fast Radial Symmetry for Detecting Points of Interest”, PAMI, 2003

  7. 3D Localisation • Single-view manhole detections are grouped under 3D geometric constraints. Projected image Localised manhole ∑ ( ) =

  8. Evaluation • 317 manholes and 270 non-manholes images in testing set. • Detection rate increases with the number of views available for each manhole. • While the single-view detection rate is about 41%, the multi-view evaluation shows 97% manhole detection rate for very low false alarm rate.

  9. Demo

  10. Conclusions • Manhole Detection, Recognition and 3D Localisation is a challenging problem. • We propose a multi-view scheme, which combines 2D and 3D analysis. • Work in progress…

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