Online approximate model representation of unknown objects

Object representation is useful for many computer vision tasks, such as object detection, recognition, and tracking. Computer vision tasks must handle situations where unknown objects appear and must detect and track some object which is not in the trained database. In such cases, the system must learn or, otherwise derive, descriptions of new objects. In this paper, we investigate creating a representation of previously unknown objects that newly appear in the scene. The representation creates a viewpoint-invariant and scale-normalized model approximately describing an unknown object with multimodal sensors. Those properties of the representation facilitate 3D tracking of the object using 2D-to-2D image matching. The representation has both benefits of an implicit model (referred to as a view-based model) and an explicit model (referred to as a shape-based model). Experimental results demonstrate the viability of the proposed representation and outperform the existing approaches for 3D-pose estimation.

[1]  Marc Pollefeys,et al.  Interactive 3D architectural modeling from unordered photo collections , 2008, SIGGRAPH 2008.

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[4]  Roland Siegwart,et al.  A comparison of line extraction algorithms using 2D range data for indoor mobile robotics , 2007, Auton. Robots.

[5]  Takeo Kanade,et al.  Boundary detection based on supervised learning , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Katsushi Ikeuchi,et al.  Object shape and reflectance modeling from observation , 1997, SIGGRAPH.

[7]  Richard Bowden,et al.  Simultaneous modeling and tracking (SMAT) of feature sets , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.

[9]  Robert T. Collins,et al.  On-the-fly Object Modeling while Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Andrew E. Johnson,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[11]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[12]  Daniel Scharstein,et al.  Matching images by comparing their gradient fields , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[13]  Cordelia Schmid,et al.  3D object modeling and recognition using affine-invariant patches and multi-view spatial constraints , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[14]  Joseph L. Mundy,et al.  Object Recognition in the Geometric Era: A Retrospective , 2006, Toward Category-Level Object Recognition.

[15]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[16]  Takeo Kanade,et al.  Extrinsic calibration of a single line scanning lidar and a camera , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  SchindlerKonrad,et al.  Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles , 2008 .

[18]  Hans-Hellmut Nagel,et al.  Model-based object tracking in monocular image sequences of road traffic scenes , 1993, International Journal of Computer 11263on.

[19]  Luc Van Gool,et al.  Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[21]  Luc Van Gool,et al.  Object Detection and Tracking for Autonomous Navigation in Dynamic Environments , 2010, Int. J. Robotics Res..

[22]  Victor S. Lempitsky,et al.  Seamless Mosaicing of Image-Based Texture Maps , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[24]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[25]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .