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» Learning to Track with Multiple Observers
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CVPR
2010
IEEE
13 years 8 months ago
Scene understanding by statistical modeling of motion patterns
We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of pattern...
Imran Saleemi, Lance Hartung, Mubarak Shah
ICCV
2009
IEEE
13 years 5 months ago
SURF Tracking
Most motion-based tracking algorithms assume that objects undergo rigid motion, which is most likely disobeyed in real world. In this paper, we present a novel motionbased trackin...
Wei He, Takayoshi Yamashita, Hongtao Lu, Shihong L...
TSMC
2008
128views more  TSMC 2008»
13 years 7 months ago
Adaptive Sensor Placement and Boundary Estimation for Monitoring Mass Objects
Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could...
Zhen Guo, MengChu Zhou, Guofei Jiang
ICMLC
2010
Springer
13 years 6 months ago
Multiple kernel learning and feature space denoising
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denois...
Fei Yan, Josef Kittler, Krystian Mikolajczyk
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 9 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider