Efficient tracking using a robust motion estimation technique

Camera based supervision is a critical part of event detection and analysis applications. However, visual tracking still remains one of the biggest challenges in the area of computer vision, although it has been extensively discussed during in the previous years. In this paper we propose a robust tracking approach based on object flow, which is a motion model for estimating both the displacement and the direction of an object of interest. In addition, an observation model that utilizes a generative prior is adopted to tackle the pitfalls that derive from the appearance changes of the object under study. The efficiency of our technique is demonstrated using sequences captured in a complex industrial environment. The experimental results show that the proposed algorithm is sound, yielding improved performance in comparison with other tracking approaches.

[1]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[2]  Roger Davies,et al.  A Machine Vision Quality Control System for Industrial Acrylic Fibre Production , 2002, EURASIP J. Adv. Signal Process..

[3]  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.

[4]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[5]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, CVPR.

[6]  Luc Van Gool,et al.  Automatic Workflow Monitoring in Industrial Environments , 2010, ACCV.

[7]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[8]  Theodora A. Varvarigou,et al.  A dataset for workflow recognition in industrial scenes , 2011, 2011 18th IEEE International Conference on Image Processing.

[9]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[10]  Junzhou Huang,et al.  Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization , 2010, ECCV.

[11]  Georgios Tziritas,et al.  Equivalent Key Frames Selection Based on Iso-Content Principles , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Ming Yang,et al.  Detection driven adaptive multi-cue integration for multiple human tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Joaquin Quiñonero Candela,et al.  Local distance preservation in the GP-LVM through back constraints , 2006, ICML.

[14]  Luc Van Gool,et al.  Object Flow: Learning Object Displacement , 2010, ACCV Workshops.

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

[16]  James J. Little,et al.  Tracking and recognizing actions of multiple hockey players using the boosted particle filter , 2009, Image Vis. Comput..

[17]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Horst Bischof,et al.  Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..

[19]  Zhenbang Gong,et al.  Data-logging and Monitoring of Production Auto-lines Based on Visual-tracking Tech , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

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

[21]  Alberto Landi,et al.  Vision system for monitoring the production of corrugated cardboard , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[22]  Luc Van Gool,et al.  Online classification of visual tasks for industrial workflow monitoring , 2011, Neural Networks.

[23]  R. Osorio,et al.  Invariant Object Recognition Robot Vision System for Assembly , 2006, Electronics, Robotics and Automotive Mechanics Conference (CERMA'06).

[24]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Jeffrey Too Chuan Tan,et al.  Triple stereo vision system for safety monitoring of human-robot collaboration in cellular manufacturing , 2011, 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM).

[26]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Björn Stenger,et al.  Tracking Using Online Feature Selection and a Local Generative Model , 2007, BMVC.

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

[29]  Frank Dellaert,et al.  An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets , 2004, ECCV.

[30]  Trevor Darrell,et al.  Rank priors for continuous non-linear dimensionality reduction , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Luc Van Gool,et al.  Coupled Detection and Trajectory Estimation for Multi-Object Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[32]  Luc Van Gool,et al.  Robust tracking-by-detection using a detector confidence particle filter , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Daniel F. García,et al.  Machine Vision System for Flatness Control Feedback , 2009, 2009 Second International Conference on Machine Vision.

[34]  Luc Van Gool,et al.  Tools for semi-automatic monitoring of industrial workflows , 2010, ARTEMIS '10.

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

[36]  S. L. Phung,et al.  A novel skin color model in YCbCr color space and its application to human face detection , 2002, Proceedings. International Conference on Image Processing.

[37]  Guo Li,et al.  Tracking video objects with feature points based particle filtering , 2010, Multimedia Tools and Applications.

[38]  J. Sentieiro,et al.  A computer vision system for the characterization and classification of flames in glass furnaces , 1991, Conference Record of the 1991 IEEE Industry Applications Society Annual Meeting.

[39]  Jean-Marc Odobez,et al.  Embedding Motion in Model-Based Stochastic Tracking , 2004, IEEE Transactions on Image Processing.

[40]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Anastasios D. Doulamis,et al.  Dynamic tracking re-adjustment: a method for automatic tracking recovery in complex visual environments , 2010, Multimedia Tools and Applications.