CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Swarm intelligence
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
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
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Differential Tracking based on Spatial-Appearance Model (SAM)
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Optimal Kernel-based Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Dependent Multiple Cue Integration for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
A Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Expert Systems with Applications: An International Journal
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
Recent advances and trends in visual tracking: A review
Neurocomputing
On pedestrian detection and tracking in infrared videos
Pattern Recognition Letters
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Towards robust multi-cue integration for visual tracking
Machine Vision and Applications
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This paper presents a multi-cue based particle swarm optimization (PSO) guided particle filter (PF) tracking framework. In the proposed tracking framework, PSO is incorporated into the probabilistic framework of PF as an optimization scheme for the propagation of particles, which can make particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously the newest observations are utilized to update the relocated particles in the update stage. Furthermore, likelihood measure functions employing multi-cue are explored to improve the robustness and accuracy of tracking. Here, each cue weight is self-adaptively adjusted by PSO algorithm throughout the tracking process. Experiments performed on several challenging public infrared video sequences demonstrate that our proposed tracking approach achieves considerable performances.