Object Modelling and Tracking in Videos via Multidimensional Features

We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. Consideration has also been made to a number of relevant aspects of object tracking including multidimensional features and the mixture of colours, textures, and object motion. The experiment of the proposed method on the video sequences has been conducted and has shown its effectiveness in capturing the target in a moving background and with nonrigid object motion.

[1]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Li Guangyao,et al.  Geometric active contours without re-initialization for image segmentation , 2009 .

[3]  Thierry Chateau,et al.  Realtime Kernel based Tracking , 2009 .

[4]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

[5]  Arnold W. M. Smeulders,et al.  Fast occluded object tracking by a robust appearance filter , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Federico Tombari,et al.  A Template Analysis Methodology to Improve the Efficiency of Fast Matching Algorithms , 2009, ACIVS.

[7]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[8]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

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

[10]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Aishy Amer.  New Binary Morphological Operations for Effective Low-Cost Boundary Detection , 2003, Int. J. Pattern Recognit. Artif. Intell..

[12]  Xiao-Ping Zhang,et al.  Segmentation of bright targets using wavelets and adaptive thresholding , 2001, IEEE Trans. Image Process..

[13]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Zhuhan Jiang,et al.  Tracking Camouflaged Objects with Weighted Region Consolidation , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).

[15]  Yuan Yang,et al.  Learning complex background by multi-scale discriminative model , 2009, Pattern Recognit. Lett..

[16]  Chaomin Shen,et al.  Color Image Segmentation for Objects of Interest with Modified Geodesic Active Contour Method , 2006, Journal of Mathematical Imaging and Vision.

[17]  James M. Rehg,et al.  A multiple hypothesis approach to figure tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[18]  Larry S. Davis,et al.  Efficient Kernel Density Estimation Using the Fast Gauss Transform with Applications to Color Modeling and Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Jean-Christophe Olivo-Marin,et al.  Coupled parametric active contours , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Zoran Duric,et al.  Using histograms to detect and track objects in color video , 2001, Proceedings 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery.

[21]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Joo Kooi Tan,et al.  Extracting moving objects from a video by sequential background detection employing a local correlation map , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[24]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Yanxi Liu,et al.  Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..