Face-TLD: Tracking-Learning-Detection applied to faces

A novel system for long-term tracking of a human face in unconstrained videos is built on Tracking-Learning-Detection (TLD) approach. The system extends TLD with the concept of a generic detector and a validator which is designed for real-time face tracking resistent to occlusions and appearance changes. The off-line trained detector localizes frontal faces and the online trained validator decides which faces correspond to the tracked subject. Several strategies for building the validator during tracking are quantitatively evaluated. The system is validated on a sitcom episode (23 min.) and a surveillance (8 min.) video. In both cases the system detects-tracks the face and automatically learns a multi-view model from a single frontal example and an unlabeled video.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Ming-Hsuan Yang,et al.  Incremental Learning for Visual Tracking , 2004, NIPS.

[3]  Jiri Matas,et al.  Online learning of robust object detectors during unstable tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[4]  Jiri Matas,et al.  Weighted Sampling for Large-Scale Boosting , 2008, BMVC.

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

[6]  Andrew Zisserman,et al.  Taking the bite out of automated naming of characters in TV video , 2009, Image Vis. Comput..

[7]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[8]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, CVPR.

[9]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[10]  Vladimir Pavlovic,et al.  Face tracking and recognition with visual constraints in real-world videos , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.