Pictorial Structures for Object Recognition
International Journal of Computer Vision
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
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
Logarithmic regret algorithms for online convex optimization
Machine Learning
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parsing human motion with stretchable models
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hough-based tracking of non-rigid objects
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Strong supervision from weak annotation: Interactive training of deformable part models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
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In this paper, we propose to track multiple previously unseen objects in unconstrained scenes. Instead of considering objects individually, we model objects in mutual context with each other to benefit robust and accurate tracking. We introduce a unified framework to combine both Individual Object Models (IOMs) and Mutual Relation Models (MRMs). The MRMs consist of three components, the relational graph to indicate related objects, the mutual relation vectors calculated within related objects to show the interactions, and the relational weights to balance all interactions and IOMs. As MRMs are varying along temporal sequences, we propose online algorithms to make MRMs adapt to current situations. We update relational graphs through analyzing object trajectories and cast the relational weight learning task as an online latent SVM problem. Extensive experiments on challenging real world video sequences demonstrate the efficiency and effectiveness of our framework.