Adapting generic trackers for tracking faces

A novel system for adapting generic trackers for long-term face tracking in unconstrained videos is proposed. The system treats the tracker as a black box. The only requirement is that the tracker can be reinitialized when needed. The system consists of a generic face detector trained offline and a validator trained online which helps to distinguish the target face from other people's faces and the background. We demonstrate this method on three state-of-the-art generic trackers: OpenTLD, Struck and MIL. For the experiments we use public face videos as well as our own dataset. In all our experiments our face tracking adaptation method shows superior results in comparison with the original trackers.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  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.

[3]  John W. McDonough,et al.  A joint particle filter for audio-visual speaker tracking , 2005, ICMI '05.

[4]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Vibhav Vineet,et al.  Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Judea Pearl,et al.  Some Recent Results in Heuristic Search Theory , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Anil K. Jain,et al.  Face Tracking and Recognition at a Distance: A Coaxial and Concentric PTZ Camera System , 2013, IEEE Transactions on Information Forensics and Security.

[9]  Jiri Matas,et al.  Face-TLD: Tracking-Learning-Detection applied to faces , 2010, 2010 IEEE International Conference on Image Processing.

[10]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[12]  Jongmoo Choi,et al.  Real-time 3-D face tracking and modeling from awebcam , 2012, 2012 IEEE Workshop on the Applications of Computer Vision (WACV).

[13]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[14]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Verfassung der Arbeit,et al.  Robust Object Tracking Based on Tracking-Learning-Detection , 2012 .

[16]  Alberto Del Bimbo,et al.  Improving evidential quality of surveillance imagery through active face tracking , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[17]  Dimitris N. Metaxas,et al.  Optical Flow Constraints on Deformable Models with Applications to Face Tracking , 2000, International Journal of Computer Vision.

[18]  Simone Calderara,et al.  Visual Tracking: An Experimental Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.