Fast occluded object tracking by a robust appearance filter

We propose a new method for object tracking in image sequences using template matching. To update the template, appearance features are smoothed temporally by robust Kalman filters, one to each pixel. The resistance of the resulting template to partial occlusions enables the accurate detection and handling of more severe occlusions. Abrupt changes of lighting conditions can also be handled, especially when photometric invariant color features are used, The method has only a few parameters and is computationally fast enough to track objects in real time.

[1]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Hai Tao,et al.  Dynamic layer representation with applications to tracking , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  Patrick Pérez,et al.  Towards Improved Observation Models for Visual Tracking: Selective Adaptation , 2002, ECCV.

[5]  Marcel Worring,et al.  Occlusion Robust Adaptive Template Tracking , 2001, ICCV.

[6]  Rama Chellappa,et al.  Simultaneous tracking and verification via sequential posterior estimation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Arnold W. M. Smeulders,et al.  Color Invariance , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[9]  Geoffrey E. Hinton,et al.  Parameter estimation for linear dynamical systems , 1996 .

[10]  Ying Wu,et al.  A co-inference approach to robust visual tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[12]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[13]  Takeo Kanade,et al.  Visual tracking of a moving target by a camera mounted on a robot: a combination of control and vision , 1993, IEEE Trans. Robotics Autom..

[14]  Michael J. Black,et al.  Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion , 1997, International Journal of Computer Vision.

[15]  Arnold W. M. Smeulders,et al.  Template tracking using color invariant pixel features , 2002, Proceedings. International Conference on Image Processing.

[16]  Arnold W. M. Smeulders,et al.  PicToSeek: combining color and shape invariant features for image retrieval , 2000, IEEE Trans. Image Process..

[17]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .

[18]  Frank Dellaert,et al.  Jacobian images of super-resolved texture maps for model-based motion estimation and tracking , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[19]  Andrew Blake,et al.  A framework for spatiotemporal control in the tracking of visual contours , 1993, International Journal of Computer Vision.

[20]  Tzay Y. Young,et al.  A Mathematical Model for Computer Image Tracking , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.