ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Quasi-Random Sampling for Condensation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Using Particles to Track Varying Numbers of Interacting People
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking Non-Stationary Appearances and Dynamic Feature Selection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
On-Line Density-Based Appearance Modeling for Object Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM Computing Surveys (CSUR)
International Journal of Computer Vision
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent advances and trends in visual tracking: A review
Neurocomputing
Heavy-Tailed model for visual tracking via robust subspace learning
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A robust particle tracker via markov chain monte carlo posterior sampling
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Real-time visual tracking based on an appearance model and a motion mode
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
Abrupt motion tracking using a visual saliency embedded particle filter
Pattern Recognition
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We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional tracking methods to fail since they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this algorithm into the Markov Chain Monte Carlo based tracking method. Our tracking method alleviates the motion smoothness constraint utilizing both the likelihood term and the density of states term, which is estimated by the Wang-Landau algorithm. The likelihood term helps to improve the accuracy in tracking smooth motions, while the density of states term captures abrupt motions robustly. Experimental results reveal that our approach efficiently samples the object's states even in a whole state space without loss of time. Therefore, it tracks the object of which motion is drastically changing, accurately and robustly.