Viewpoint Tracking for 3D Display Systems A look at the system proposed by Yusuf Bediz, Gözde Bozdağı Akar

Outline Why viewpoint tracking? Proposed System  Object Detection  Tracking  Viewpoint Calculation Results Improvements Conclusion

Why viewpoint tracking? Parallax Increased accuracy in views Lower cost

Proposed System Train specialized feature trackers to detect eyes Determine viewpoint Update view

Object Detection Overview Training based on positive images of several peoples faces, negative images of backgrounds Uses suite of linear classifier such as svms to classify ‘features’ such as eyes or face Makes use of Adaboost to perform several re-weighted simple linear classifiers

Object Detection Training

Reducing Dimensionality Two Rectangle Method Lossy Compression RGB to Grayscale Resolution of the Image Reduction

Object Detection - Classification Windows of an image are selected and resized 2 Rectangle calculation is performed Result is passed into a SVM for Classification Cascading classification occurs on all windows.

Object Detection - Results 96.8 % Accurate with 6 false detections on a BIOId dataset.

Tracking Modified Lucas Kanade pyramidal tracking algorithm

Viewpoint Calculation As only the viewing angle is needed no depth estimation is required.

Results 95+% Accuracy on detection ms for detection phase 7-8 ms for tracking phase Overall system runs at 20 fps

Improvements Allow Y as well as X variation in viewpoint Improved tracking algorithm Improve framerate

Questions?

References Lucas B D and Kanade T 1981, An iterative image registration technique with an application to stereo vision. Proceedings of Imaging understanding workshop, pp J.-Y. Bouguet. “Pyramidal implementation of the Lucas Kanade feature tracker”, OpenCV Documentation, Microprocessor Labs, Intel Corp., 2000 Toyama, K. and Hager, G. D Incremental Focus of Attention for Robust Vision-Based Tracking. Int. J. Comput. Vision 35, 1 (Nov. 1999), DOI= Paul Viola, Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features” 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1 Qiong Wang, Jingyu Yang, and Wankou Yang, “Face Detection using Rectangle Features and SVM”, International Journal of Intelligent Technology Volume 1 Number 3, 2006