Combination of Multiple Measurement Cues for Visual Face Tracking

Visual face tracking is an important building block for all intelligent living and working spaces, as it is able to locate persons without any human intervention or the need for the users to carry sensors on themselves. In this paper we present a novel face tracking system built on a particle filtering framework that facilitates the use of non-linear visual measurements on the facial area. We concentrate on three different such non-linear visual measurement cues, namely object detection, foreground segmentation and colour matching. We derive robust measurement likelihoods under a unified representation scheme and fuse them into our face tracking algorithm. This algorithm is complemented with optimum selection of the particle filter’s object model and a target handling scheme. The resulting face tracking system is extensively evaluated and compared to baseline ones.

[1]  Montse Pardàs,et al.  Shadow removal with blob-based morphological reconstruction for error correction , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[2]  Anna Freud,et al.  Design And Analysis Of Modern Tracking Systems , 2016 .

[3]  Stefan Poslad,et al.  Personalized Coverage of Large Athletic Events , 2011, IEEE MultiMedia.

[4]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 2004, International Journal of Computer Vision.

[5]  Aristodemos Pnevmatikakis,et al.  Person Tracking , 2009, Computers in the Human Interaction Loop.

[6]  Alain Crouzil,et al.  Non-rigid object localization from color model using mean shift , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[8]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[9]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[10]  A. Pnevmatikakis,et al.  Robust Estimation of Background for Fixed Cameras , 2006, 2006 15th International Conference on Computing.

[11]  Lifeng Sun,et al.  A cascade SVM approach for head-shoulder detection using histograms of oriented gradients , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[12]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  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.

[14]  Gary Bradski,et al.  Learning-Based Computer Vision with Intels Open Source Computer Vision Library , 2005 .

[15]  Alexander H. Waibel.  CHIL - Computers in the Human Interaction Loop , 2005, MVA.

[16]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.