CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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
Robust Face Detection at Video Frame Rate Based on Edge Orientation Features
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IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
Removing photography artifacts using gradient projection and flash-exposure sampling
ACM SIGGRAPH 2005 Papers
An incremental Bhattacharyya dissimilarity measure for particle filtering
Pattern Recognition
Likelihood tuning for particle filter in visual tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Trajectory-based representation of human actions
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Marker-based human motion capture in multiview sequences
EURASIP Journal on Advances in Signal Processing
Effective appearance model and similarity measure for particle filtering and visual tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Using Segmented 3D Point Clouds for Accurate Likelihood Approximation in Human Pose Tracking
International Journal of Computer Vision
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Since the introduction of particle filtering for object tracking, a lot of improvements have been suggested. However, the definition of the observation likelihood function, needed for determining the particle weights, has received little attention. Because particle weights determine how the particles are re-sampled, the likelihood function has a strong influence on the tracking performance. We show experimental results for three different tracking tasks for different parameter values of the assumed observation model. The results show a large influence of the model parameters on the tracking performance. Optimizing the likelihood function can give significant tracking improvement. Different optimal parameter settings are observed for the three different tracking tasks. Consequently, when performing multiple tasks a tradeoff must be made for the parameter setting. In practical situations where robust tracking must be achieved with a limited amount of particles, the true observation probability is not always the optimal likelihood function.