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TIP
2010
141views more  TIP 2010»
13 years 2 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina
ICCV
2011
IEEE
12 years 7 months ago
Multi-hypothesis Motion Planning for Visual Object Tracking
In this paper, we propose a long-term motion model for visual object tracking. In crowded street scenes, persistent occlusions are a frequent challenge for tracking algorithm and ...
Haifeng Gong, Jack Sim, Maxim Likhachev, Jianbo Sh...
IDT
2008
123views more  IDT 2008»
13 years 7 months ago
A rich semantic model of track as a foundation for sharing beliefs regarding dynamic objects and events
: Many defense, homeland security, and commercial security objectives require continuous tracking of mobile entities such as aircraft. The systems that perform these functions prod...
Frederick Hayes-Roth, Curtis L. Blais
ECAI
2008
Springer
13 years 9 months ago
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Abstract. We propose a framework that learns functional objectes from spatio-temporal data sets such as those abstracted from video. The data is represented as one activity graph t...
Muralikrishna Sridhar, Anthony G. Cohn, David C. H...
IPSN
2005
Springer
14 years 1 months ago
Tracking on a graph
Abstract— This paper considers the problem of tracking objects with sparsely located binary sensors. Tracking with a sensor network is a challenging task due to the inaccuracy of...
Songhwai Oh, Shankar Sastry