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AVSS
2006
IEEE
13 years 11 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
ICPR
2008
IEEE
14 years 1 months ago
Multivariate Laplace Filter: A heavy-tailed model for target tracking
Video-based target tracking is a challenging task, because there always appears to be complex occlusion among the varying number of objects. Also, in practice, it is very common t...
Daojing Wang, Chao Zhang, Xuemin Zhao
BMVC
1998
13 years 8 months ago
Learning Enhanced 3D Models for Vehicle Tracking
This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic obj...
James M. Ferryman, Anthony D. Worrall, Stephen J. ...
ICIP
2008
IEEE
14 years 1 months ago
Matching tracking sequences across widely separated cameras
In this paper, we present a new solution to the problem of matching tracking sequences across different cameras. Unlike snapshot-based appearance matching which matches objects by...
Yinghao Cai, Kaiqi Huang, Tieniu Tan
PREMI
2007
Springer
14 years 1 months ago
An Adaptive Bayesian Technique for Tracking Multiple Objects
Abstract. Robust tracking of objects in video is a key challenge in computer vision with applications in automated surveillance, video indexing, human-computer-interaction, gesture...
Pankaj Kumar, Michael J. Brooks, Anton van den Hen...