CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Simulated Annealing: A Proof of Convergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Optimization and Filtering for Human Motion Capture
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Discriminative spatial attention for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Robust Visual Tracking and Vehicle Classification via Sparse Representation
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
Scribble Tracker: A Matting-Based Approach for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
Real time robust L1 tracker using accelerated proximal gradient approach
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Blurred target tracking by Blur-driven Tracker
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The particle filter is an effective approach for virtual object tracking. However, it suffers from the inaccuracy of tracking performance and track drifts which are caused by the inaccurate dynamic model. In irregular motion tracking, because of the large motion uncertainty and the poor prediction of the dynamic model, these two problems will definitely occur. We propose to model the object motion by an implicit dynamic model which is optimized by an iterative optimization method. We observe that the state with the biggest value of the sum of all particles' likelihoods will reach or be close to the ground truth which is testified by many experiments. Based on this, the dynamic model is formulated by maximizing an objective function. By evolving particles to new positions to obtain the maxima of summed particle likelihood, this particle shift strategy considerably improves the sampling efficiency. Extensive experiments show that the proposed algorithm outperforms other six trackers in dealing with irregular motions.