Real-time tracking of image regions with changes in geometry and illumination

Historically, SSD or correlation-based visual tracking algorithms have been sensitive to changes in illumination and shading across the target region. This paper describes methods for implementing SSD tracking that is both insensitive to illumination variations and computationally efficient. We first describe a vector-space formulation of the tracking problem, showing how to recover geometric deformations. We then show that the same vector space formulation can be used to account for changes in illumination. We combine geometry and illumination into an algorithm that tracks large image regions on live video sequences using no more computation than would be required to trade with no accommodation for illumination changes. We present experimental results which compare the performance of SSD tracking with and without illumination compensation.

[1]  Berthold K. P. Horn,et al.  Determining Shape and Reflectance Using Multiple Images , 1978 .

[2]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[3]  Robert J. Woodham,et al.  Analysing Images of Curved Surfaces , 1981, Artif. Intell..

[4]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[5]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[6]  James M. Rehg,et al.  Visual tracking with deformation models , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[7]  A. Shashua.  Geometry and Photometry in 3D Visual Recognition , 1992 .

[8]  Peter K. Allen,et al.  Hand-Eye Coordination for Robotic Tracking and Grasping , 1993 .

[9]  Takeo Kanade,et al.  Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision , 1993, IEEE Trans. Robotics Autom..

[10]  Peter W. Hallinan.  A low-dimensional representation of human faces for arbitrary lighting conditions , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Nikolaos Papanikolopoulos,et al.  Controlled active exploration of uncalibrated environments , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Gregory D. Hager,et al.  Robot hand-eye coordination based on stereo vision , 1995 .

[14]  E. Huber,et al.  Using Stereo Vision to Pursue Moving Agents with a Mobile Robot , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[15]  Michael J. Black,et al.  Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[16]  David Kortenkamp,et al.  Using stereo vision to pursue moving agents with a mobile robot , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[17]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, ECCV.

[18]  David J. Kriegman,et al.  What is the set of images of an object under all possible lighting conditions? , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.