Video object tracking using adaptive Kalman filter
By Shiuh-Ku Weng, Chung-Ming Kuo, S. Tu. Journal of Visual Communication and Image Representation, 2006
338 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/680e2e0a670068a76d066c0a4315dc8016342669
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Jitendra Malik
- Sergey Levine
- Pulkit Agrawal
- Katerina Fragkiadaki
- Panna Felsen
- Abhinav Kumar Gupta
- Kristen Grauman
- Pieter Abbeel
In Computer Vision and Pattern Recognition
- Jitendra Malik
- Luc Van Gool
- Andrew Zisserman
- Andrea Vedaldi
- Abhinav Kumar Gupta
- Trevor Darrell
- Kristen Grauman
- Xiaogang Wang
Citations in the corpus
- Learning Visual Predictive Models of Physics for Playing Billiards (2015, background): “Standard smoothers or filters, such as Kalman filters (Weng et al., 2006), usually operate on Cartesian coordinates, rather than the visual content of the targets, and in this way discard useful information that may be present in the images.”
- Recurrent Network Models for Human Dynamics (2015, methodology): “Parametric temporal filters such as Kalman filtering [47], HMMs or Gaussian processes for activity specific dynamics [39, 19, 28] generally use simple, linear dynamics models for prediction.”
- What will Happen Next? Forecasting Player Moves in Sports Videos (2017, background): “A large body of work has focused on action recognition [5, 42, 20, 31, 6, 30], people and object tracking [33, 41, 43].”