IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Collaborative Kernel Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multiple hypothesis video segmentation from superpixel flows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Track to the future: Spatio-temporal video segmentation with long-range motion cues
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Tracking objects under occlusion or non-rigid deformation poses a major problem: appearance variation of target makes existing bounding rectangle based representation vulnerable to background noise imported during adaptive appearance update. We address the object tracking problem by exploring superpixel based visual information around the target. Instead of representing each object with a single holistic appearance model, we propose to track each target with multiple related parts and model the tracking system as a Dynamic Bayesian Network(DBN). Based on visual features from superpixels, we propose a constellation appearance model with multiple parts which is adaptable to appearance variations. A particle-based approximate inference algorithm over the DBN is proposed for tracking. Experimental results show that the proposed algorithm performs favorably against existing object trackers especially during deformation and occlusion.