An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Intelligent Collaborative Tracking by Mining Auxiliary Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
On-line ensemble SVM for robust object tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Online visual tracking with histograms and articulating blocks
Computer Vision and Image Understanding
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust tracking using local sparse appearance model and K-selection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Structure information has been increasingly incorporated into computer vision field, whereas only a few tracking methods have employed the inner structure of the target. In this paper, we introduce a dynamic graph with pairwise Markov property to model the structure information between the inner parts of the target. The target tracking is viewed as tracking a dynamic undirected graph whose nodes are the target parts and edges are the interactions between parts. These target parts within the graph waiting for matching are separated from the background with graph cut, and a spectral matching technique is exploited to accomplish the graph tracking. With the help of an intuitive updating mechanism, our dynamic graph can robustly adapt to the variations of target structure. Experimental results demonstrate that our structured tracker outperforms several state-of-the-art trackers in occlusion and structure deformations.