Distant object recognition with Grabcut for an active-zooming camera

In this paper a method for distant object recognition is proposed. We propose an iterative structure for distant object recognition using an active-zooming camera like PTZ camera. We adopt Graphcut-based segmentation algorithm, Grabcut, to our structure in order to select candidate regions for zooming and gazing. Grabcut is utilized through the modified strategy for our purpose. And we also propose two failure detection methods based on contour points and homography for our object recognition system. We show that our candidate region selection method is available for distance change from the camera, and also validate our recognition method through the real experiment.

[1]  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).

[2]  Simone Frintrop,et al.  Attentional Landmarks and Active Gaze Control for Visual SLAM , 2008, IEEE Transactions on Robotics.

[3]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[4]  Bernhard Rinner,et al.  Video Analysis in Pan-Tilt-Zoom Camera Networks , 2010, IEEE Signal Processing Magazine.

[5]  Sameer Singh,et al.  A survey of object recognition methods for automatic asset detection in high-definition video , 2010, 2010 IEEE 9th International Conference on Cyberntic Intelligent Systems.

[6]  Danica Kragic,et al.  Integrating Active Mobile Robot Object Recognition and SLAM in Natural Environments , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  B. Cyganek An Introduction to 3D Computer Vision Techniques and Algorithms , 2009 .

[9]  Chandana Paul,et al.  Hybrid laser and vision based object search and localization , 2008, 2008 IEEE International Conference on Robotics and Automation.

[10]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[11]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[12]  Boguslaw Cyganek,et al.  Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain , 2007, IbPRIA.

[13]  Moonhong Baeg,et al.  An object recognition system for a smart home environment on the basis of color and texture descriptors , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Changchang Wu,et al.  SiftGPU : A GPU Implementation of Scale Invariant Feature Transform (SIFT) , 2007 .

[15]  Nick G. Kingsbury,et al.  Coarse-level object recognition using interlevel products of complex wavelets , 2005, IEEE International Conference on Image Processing 2005.

[16]  Qijun Chen,et al.  Vision-based fast objects recognition and distances calculation of robots , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[17]  Marc Friedman,et al.  Video Surveillance for Biometrics: Long-Range Multi-biometric System , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[18]  Gérard G. Medioni,et al.  Real time tracking using an active pan-tilt-zoom network camera , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

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

[21]  Charless C. Fowlkes,et al.  Multiresolution Models for Object Detection , 2010, ECCV.

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