CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning
International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Online Selecting Discriminative Tracking Features Using Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Proposal of novel histogram features for face detection
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Body posture estimation in sign language videos
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
An Efficient Particle Filter---based Tracking Method Using Graphics Processing Unit (GPU)
Journal of Signal Processing Systems
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Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can be computed at negligible cost. However, straightforward application of the Integral Histogram Images causes the problem of "curse of dimensionality". In addition, traditional histogram is inefficient and inaccurate. Thus we propose to adaptively determine histogram bins based on K-Means clustering, which can represent color distribution of object more compactly and accurately with as a small number of bins. Thanks to the Integral Histogram Images and the clustering based color histogram, we finally achieve a fast and robust particle filter algorithm for object tracking. Experiments show that the performance of the algorithm is encouraging.