Sequential Monte Carlo tracking by fusing multiple cues in video sequences
Image and Vision Computing
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
Predicting Articulated Human Motion from Spatial Processes
International Journal of Computer Vision
Data-driven importance distributions for articulated tracking
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
3D Human model adaptation by frame selection and shape-texture optimization
Computer Vision and Image Understanding
Spatial measures between human poses for classification and understanding
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
A novel evidence based model for detecting dangerous situations in level crossing environments
Expert Systems with Applications: An International Journal
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Statistical inefficiency often limits the effectiveness ofparticle filters for high-dimensional Bayesian trackingproblems. To improve sampling efficiency on continuousdomains, we propose the use of a particle filter with hybridMonte Carlo (HMC), an MCMC method that followsposterior gradients toward high probability states, whileensuring a properly weighted approximation to the poste-rior.We use HMC filtering to infer the 3D shape and motionof people from natural, monocular image sequences. Theapproach currently uses an empirical, edge-based likelihoodfunction, and a second-order dynamical model withsoft bio-mechanical joint constraints.