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
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Gaussian sum particle filtering
IEEE Transactions on Signal Processing
Predictive head movement tracking using a Kalman filter
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
High-performance template tracking
Journal of Visual Communication and Image Representation
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Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, generic particle filter (GPF) is based on Monte Carlo approach and sampling is a problematic issue. This paper introduces a parzen particle filter (PPF) which uses a general kernel approach to better approximate the posterior distribution rather than Dirac delta kernel in GPF. Furthermore, we adopt multiple cues and combine texture described by directional energy from multiscale, multi-orientation steerable filtering with color to characterize our tracking targets. The advantages of tracking with multiple cues compared to individual ones are demonstrated over experiments on artificial and natural sequences.