Active contours for video object tracking using region, boundary and shape information

In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback–Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences.

[1]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[3]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[4]  Daniel Cremers,et al.  Dynamical statistical shape priors for level set-based tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Anthony J. Yezzi,et al.  Fast incorporation of optical flow into active polygons , 2005, IEEE Transactions on Image Processing.

[6]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[7]  Michel Barlaud,et al.  DREAM2S: Deformable Regions Driven by an Eulerian Accurate Minimization Method for Image and Video Segmentation , 2002, ECCV.

[8]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[9]  Michael Isard,et al.  Active Contours , 2000, Springer London.

[10]  Stefano Soatto,et al.  A Pseudo-distance for Shape Priors in Level Set Segmentation , 2003 .

[11]  Djemel Ziou,et al.  Automatic Color-Texture Image Segmentation by Using Active Contours , 2006, IWICPAS.

[12]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[13]  A. Murat Tekalp,et al.  Performance measures for video object segmentation and tracking , 2003, IEEE Transactions on Image Processing.

[14]  Olivier D. Faugeras,et al.  3D Articulated Models and Multiview Tracking with Physical Forces , 2001, Comput. Vis. Image Underst..

[15]  Abdol-Reza Mansouri,et al.  Region Tracking via Level Set PDEs without Motion Computation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  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..

[17]  Jing Huang,et al.  Spatial Color Indexing and Applications , 2004, International Journal of Computer Vision.

[18]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[19]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[20]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.

[21]  G. Aubert,et al.  Detection and tracking of moving objects using a new level set based method , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[22]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[23]  C. Drewniok,et al.  Multi-spectral edge detection. Some experiments on data from Landsat-TM , 1994 .

[24]  C. Stiller,et al.  Estimating motion in image sequences , 1999, IEEE Signal Process. Mag..

[25]  J. Sethian,et al.  A Fast Level Set Method for Propagating Interfaces , 1995 .

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

[27]  Tao Zhang,et al.  Improving performance of distribution tracking through background mismatch , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Janusz Konrad,et al.  Multiple motion segmentation with level sets , 2003, IEEE Trans. Image Process..

[29]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[30]  Pascal Fua,et al.  Tracking and Modeling People in Video Sequences , 2001, Comput. Vis. Image Underst..

[31]  Rachid Deriche,et al.  Geodesic active regions and level set methods for motion estimation and tracking , 2005, Comput. Vis. Image Underst..

[32]  J. Weickert,et al.  Fast Methods for Implicit Active Contour Models , 2003 .

[33]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[35]  Touradj Ebrahimi,et al.  Tracking video objects in cluttered background , 2005, IEEE Transactions on Circuits and Systems for Video Technology.