Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Unsupervised Improvement of Visual Detectors using Co-Training
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Learning-based object tracking using boosted features and appearance-adaptive models
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Learning compositional categorization models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle Swarm Optimization Based Object Tracking
Fundamenta Informaticae - Swarm Intelligence
Efficient multi-feature PSO for fast gray level object-tracking
Applied Soft Computing
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We propose a hybrid tracking algorithm consisting of two trackers built on grayscale appearance models. In a first tracker we employ an object template that consists of several grayscale image patches. Every patch votes for the possible positions of the object undergoing tracking. A grayscale appearance model that is learned on-line is used in a supplementing tracker. A particle swarm optimization algorithm is utilized to shift particles toward more promising regions in the probability density function. Experimental results show that the hybrid tracker outperforms each of the trackers.