Active object recognition using vocabulary trees

For mobile robots to perform certain tasks in human environments, fast and accurate object classification is essential. Actively exploring objects by changing viewpoints promises an increase in the accuracy of object classification. This paper presents an efficient feature-based active vision system for the recognition and verification of objects that are occluded, appear in cluttered scenes and may be visually similar to other objects present. This system is designed using a selector-observer framework where the selector is responsible for the automatic selection of the next best viewpoint and a Bayesian `observer' updates the belief hypothesis and provides feedback. A new method for automatically selecting the `next best viewpoint' is presented using vocabulary trees. It is used to calculate a weighting for each feature based on its perceived uniqueness, allowing the system to select the viewpoint with the greatest number of `unique' features. The process is sped-up as new images are only captured at the `next best viewpoint' and processed when the belief hypothesis of an object is below some pre-defined threshold. The system also provides a certainty measure for the objects identity. This system out performs randomly selecting a viewpoint as it processes far fewer viewpoints to recognise and verify objects in a scene.

[1]  Pietro Perona,et al.  Evaluation of Features Detectors and Descriptors Based on 3D Objects , 2005, ICCV.

[2]  Avinash C. Kak,et al.  Planning sensing strategies in a robot work cell with multi-sensor capabilities , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[3]  Gert Kootstra,et al.  Active exploration and keypoint clustering for object recognition , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views , 2006, International Journal of Computer Vision.

[6]  Sven J. Dickinson,et al.  Active Object Recognition Integrating Attention and Viewpoint Control , 1997, Comput. Vis. Image Underst..

[7]  Andrea Salgian,et al.  Appearance-based object recognition using multiple views , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  Frank P. Ferrie,et al.  Active recognition: using uncertainty to reduce ambiguity , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[9]  Tsuhan Chen,et al.  Active view selection for object and pose recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[10]  Katsushi Ikeuchi,et al.  Planning multiple observations for object recognition , 2005, International Journal of Computer Vision.

[11]  Lucas Paletta,et al.  A Comparison of Probabilistic, Possibilistic and Evidence Theoretic Fusion Schemes for Active Object Recognition , 1999, Computing.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  David A. Forsyth,et al.  3D Object Recognition Using Invariance , 1995, Artif. Intell..

[14]  Pietro Perona,et al.  Evaluation of Features Detectors and Descriptors based on 3D Objects , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[15]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[16]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[18]  Lucas Paletta,et al.  Appearance-based active object recognition , 2000, Image Vis. Comput..

[19]  Subhashis Banerjee,et al.  Isolated 3D object recognition through next view planning , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Frank P. Ferrie,et al.  Active Object Recognition: Looking for Differences , 2001, International Journal of Computer Vision.

[21]  Siddhartha S. Srinivasa,et al.  Object recognition and full pose registration from a single image for robotic manipulation , 2009, 2009 IEEE International Conference on Robotics and Automation.

[22]  Roland Siegwart,et al.  Improved appearance-based matching in similar and dynamic environments using a Vocabulary tree , 2010, 2010 IEEE International Conference on Robotics and Automation.

[23]  Lucas Paletta,et al.  Active Object Recognition in Parametric Eigenspace , 1998, BMVC.