The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Incremental Focus of Attention for Robust Vision-Based Tracking
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid Monte Carlo Filtering: Edge-Based People Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Bayesian Fusion of Color and Texture Segmentations
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
A filter design technique for steerable pyramid image transforms
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local-motion-based probabilistic model for visual tracking
Pattern Recognition
A two-stage dynamic model for visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual tracking by fusing multiple cues with context-sensitive reliabilities
Pattern Recognition
On pedestrian detection and tracking in infrared videos
Pattern Recognition Letters
Fragments based tracking with adaptive cue integration
Computer Vision and Image Understanding
Shape based appearance model for kernel tracking
Image and Vision Computing
Dynamic appearance model for particle filter based visual tracking
Pattern Recognition
State estimation of a supply chain using improved resampling rules for particle filtering
Proceedings of the Winter Simulation Conference
Markov Chain Monte Carlo Modular Ensemble Tracking
Image and Vision Computing
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This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visual models is presented along with a method for adaptively weighting the cues when multiple models are used. A particle filter (PF) is designed for object tracking based on multiple cues with adaptive parameters. Its performance is investigated and evaluated with synthetic and natural sequences and compared with the mean-shift tracker. We show that tracking with multiple weighted cues provides more reliable performance than single cue tracking.