Investigation of a new method for improving image resolution for camera tracking applications

Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective. This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times.

[1]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[2]  Aggelos K. Katsaggelos,et al.  Super Resolution of Images and Video , 2006, Super Resolution of Images and Video.

[3]  Nuno Lau,et al.  Using a Depth Camera for Indoor Robot Localization and Navigation , 2011 .

[4]  Vivek Bannore Iterative-Interpolation Super-Resolution (IISR) , 2009 .

[5]  A. Murat Tekalp,et al.  Super resolution recovery for multi-camera surveillance imaging , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[6]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Andreas Nchter 3D Robotic Mapping: The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom , 2009 .

[8]  Thomas S. Huang,et al.  Image Super-Resolution: Historical Overview and Future Challenges , 2017 .

[9]  Michael Elad,et al.  Super-Resolution Reconstruction of Image Sequences , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Todd Randall Reed,et al.  Digital image sequence processing, compression, and analysis , 2004 .

[11]  Takeo Kanade,et al.  Hallucinating faces , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[12]  Michael Elad,et al.  Superresolution restoration of an image sequence: adaptive filtering approach , 1999, IEEE Trans. Image Process..

[13]  Víctor González-Pacheco,et al.  Integration of a low-cost RGB-D sensor in a social robot for gesture recognition , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[14]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[15]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[16]  Michael Elad,et al.  Super-resolution reconstruction of continuous image sequences , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[17]  B. Grothe,et al.  Structure and function of the bat superior olivary complex , 2000, Microscopy research and technique.

[18]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[19]  Andreas Nüchter,et al.  3D Robotic Mapping - The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom , 2009, Springer Tracts in Advanced Robotics.