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CVPR
2004
IEEE
14 years 7 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
ECCV
2004
Springer
14 years 7 months ago
A Boosted Particle Filter: Multitarget Detection and Tracking
The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Ga...
Kenji Okuma, Ali Taleghani, Nando de Freitas, Jame...
SIBGRAPI
2005
IEEE
13 years 11 months ago
Particle Filter-Based Predictive Tracking for Robust Fish Counting
In this paper we study the use of computer vision techniques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the application of a B...
Erikson F. Morais, Mario Fernando Montenegro Campo...
ICIP
2006
IEEE
14 years 7 months ago
A Hierarchical ASM/AAM Approach in a Stochastic Framework for Fully Automatic Tracking and Recognition
This paper deals with the fully automatic extraction of classifiable person features out of a video stream with challenging background. Basically the task can be split in two part...
Andre Störmer, Gerhard Rigoll, Sascha Schreib...
CVPR
2008
IEEE
14 years 7 months ago
Efficient mean shift belief propagation for vision tracking
A mechanism for efficient mean-shift belief propagation (MSBP) is introduced. The novelty of our work is to use mean-shift to perform nonparametric mode-seeking on belief surfaces...
Minwoo Park, Yanxi Liu, Robert T. Collins