Hand gesture recognition with motion tracking on spatial-temporal filtering
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
Visual tracking via multiple representative basic appearance models based on l 1 minimization
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Robust visual tracking using dynamic classifier selection with sparse representation of label noise
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Dynamic objectness for adaptive tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Visual tracking with representative templates based on low-rank matrix
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Tracking with a mixed continuous-discrete Conditional Random Field
Computer Vision and Image Understanding
Integrating tracking with fine object segmentation
Image and Vision Computing
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
Collaborative object tracking model with local sparse representation
Journal of Visual Communication and Image Representation
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We propose a novel tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently. The experiment demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments and outperforms the state-of-the-art tracking methods.