Robust multi-target tracking using spatio-temporal context

In multi-target tracking, the maintaining of the correct identity of targets is challenging. In the presented tracking method, accurate target identification is achieved by incorporating the appearance information of the spatial and temporal context of each target. The spatial context of a target involves local background and nearby targets. The first contribution of the paper is to provide a new discriminative model for multi-target tracking with the embedded classification of each target against its context. As a result, the tracker not only searches for the image region similar to the target but also avoids latching on nearby targets or on a background region. The temporal context of a target includes its appearances seen during tracking in the past. The past appearances are used to train a probabilistic PCA that is used as the measurement model of the target at the present. As the second contribution, we develop a new incremental scheme for probabilistic PCA. It can update accurately the full set of parameters including a noise parameter still ignored in related literature. The experiments show robust tracking performance under the condition of severe clutter, occlusions and pose changes.

[1]  Ralph R. Martin,et al.  Merging and Splitting Eigenspace Models , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  David J. Kriegman,et al.  Visual tracking using learned linear subspaces , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  Matthew Brand,et al.  Incremental Singular Value Decomposition of Uncertain Data with Missing Values , 2002, ECCV.

[4]  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).

[5]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Nicholas Roy,et al.  SLAM using Incremental Probabilistic PCA and Dimensionality Reduction , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  Ming-Hsuan Yang,et al.  Adaptive Probabilistic Visual Tracking with Incremental Subspace Update , 2004, ECCV.

[8]  Arnold W. M. Smeulders,et al.  Tracking Aspects of the Foreground against the Background , 2004, ECCV.

[9]  Ramakant Nevatia,et al.  Tracking multiple humans in crowded environment , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  Michael Isard,et al.  PAMPAS: real-valued graphical models for computer vision , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  Ming-Hsuan Yang,et al.  Adaptive Discriminative Generative Model and Its Applications , 2004, NIPS.

[12]  Timothy F. Cootes,et al.  View-based active appearance models , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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

[14]  Ming-Hsuan Yang,et al.  Incremental Learning for Visual Tracking , 2004, NIPS.

[15]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[16]  Michael Lindenbaum,et al.  Sequential Karhunen-Loeve basis extraction and its application to images , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[17]  Alex Pentland,et al.  Probabilistic visual learning for object detection , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

[19]  Ying Wu,et al.  Collaborative tracking of multiple targets , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[20]  D. Titterington.  Recursive Parameter Estimation Using Incomplete Data , 1984 .

[21]  R. Collins,et al.  On-line selection of discriminative tracking features , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[23]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Complex Visual Objects , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.