Robot vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
GPU-supported object tracking using adaptive appearance models and particle swarm optimization
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
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This paper proposes a particle swarm optimization based algorithm for object tracking in image sequences. The parametric models of variability of the object appearance are employed to shift the particle swarm in order to cover the promising object location. Afterwards the particles are drawn from a Gaussian distribution. Then the particle swarm optimization takes place in order to concentrate the particles near the true object state. A grayscale appearance model that is learned online is utilized in evaluation of the particles score. Experimental results thatwere obtained in a typical office environment show the feasibility of our approach, especially when the object undergoing tracking has a rapid motion or the appearance changes are considerable. The resulting algorithm runs in real-time on a standard computer.