Results 1 - 10
of
816
Bootstrapping the Condensation Algorithm
, 2002
"... this paper we suggest to apply bootstrapping to increase performance in model-based tracking. The bootstrapping information is in the form of the position of the hand in the image. The idea of bootstrapped tracking is exemplified in the context of monocular tracking of the 3D pose of a human arm uti ..."
Abstract
- Add to MetaCart
utilising the Condensation algorithm. A number of tests are conducted and it is concluded that bootstrapped tracking is a promising approach when solving some of the inherent problems in model-based tracking
CONDENSATION -- conditional density propagation for visual tracking
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to th ..."
Abstract
-
Cited by 1503 (12 self)
- Add to MetaCart
The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied
The Condensation algorithm................... 3
, 2009
"... Particle filter methods based on color distribution can be used to track non-rigid moving objects in color videos. They are robust in case of noise or partial occlusions. However, using particle filters on color videos is sensitive to changes in the lighting conditions of the scene. The use of therm ..."
Abstract
- Add to MetaCart
Particle filter methods based on color distribution can be used to track non-rigid moving objects in color videos. They are robust in case of noise or partial occlusions. However, using particle filters on color videos is sensitive to changes in the lighting conditions of the scene. The use of thermal infrared image sequences can help the tracking process, as thermal infrared imagery is not sensitive to lighting conditions. This paper presents a particle filter based method for object tracking using automatic cooperation between the color and the infrared modalities. As the infrared and the visible image sequences are acquired with different cameras, a pre-step is spatio-temporal registration. After spatio-temporal registration, the proposed method is able to continuously track the target despite difficult conditions appearing in one of the modality. Our cooperative tracking method is successfully applied on several experimental datasets. Different test sequences are presented, including tracking in the visible video with the help of the infrared modality, or tracking in the infrared with the help of the visible modality. Comments and future prospects raised by this method are finally
Tracking Multiple Objects Using the Condensation Algorithm
, 2001
"... Some years ago a new tracker, the Condensation algorithm, came... In this paper an extension of the Condensation algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can ow d ..."
Abstract
-
Cited by 40 (0 self)
- Add to MetaCart
Some years ago a new tracker, the Condensation algorithm, came... In this paper an extension of the Condensation algorithm is introduced that relies on a single probability distribution to describe the likely states of multiple objects. By introducing an initialization density, observations can ow
Recognizing temporal trajectories using the condensation algorithm
- In Face and Gesture Recognition
, 1998
"... The recognition of human gestures in image sequences is an important and challengingproblem that enables a host of human-computer interaction applications. This paper describes an incremental recognition strategy that is an extension of the “Condensation ” algorithm proposed by Isard and Blake (ECCV ..."
Abstract
-
Cited by 51 (2 self)
- Add to MetaCart
The recognition of human gestures in image sequences is an important and challengingproblem that enables a host of human-computer interaction applications. This paper describes an incremental recognition strategy that is an extension of the “Condensation ” algorithm proposed by Isard and Blake
Using the CONDENSATION Algorithm for Robust, Vision-based Mobile Robot Localization
, 1999
"... To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these ..."
Abstract
-
Cited by 137 (25 self)
- Add to MetaCart
-based localization method based on the CONDENSATION algorithm [17, 18], a Bayesian filtering method that uses a samplingbased density representation. We show how the CONDENSATION algorithm can be used in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
Abstract
-
Cited by 661 (23 self)
- Add to MetaCart
simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm --- Conditional
Feature Condensing Algorithm for Feature Selection ∗
"... A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster. In experiments with ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster. In experiments with real-world data, we show that the proposed method is benefical as a pre-filtering step for more sophisticated feature selection techniques. 1.
Fingers image tracking on omnidirection camera by CONDENSATION algorithm
- Proc. of the IASTED International Conference on Automation, Control, and Information Technology -Signal and Image Processing
, 2005
"... Tracking of fingers image on omnidirection camera has been investigated which aims at developing of human friendly interface using fingers ’ gesture. Where the way of using the omnidirection camera is different from a normal usage in which the user holds the camera with his/her fingers and move them ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
them. The interface can use the motion of the fingers to establish a communication between the user and computer system in human friendly way. To achieve this, it is necessary for the system to track the fingers in the dynamic image of omnidirection camera. We have employed CONDENSATION algorithm
Recognizing Temporal Trajectories using the Condensation Algorithm
, 1998
"... The recognition of human gestures in image sequences is an importantand challengingproblem that enables a host of human-computer interaction applications. This paper describes an incremental recognition strategy that is an extension of the "Condensation" algorithm proposed by Isard an ..."
Abstract
- Add to MetaCart
The recognition of human gestures in image sequences is an importantand challengingproblem that enables a host of human-computer interaction applications. This paper describes an incremental recognition strategy that is an extension of the "Condensation" algorithm proposed by Isard
Results 1 - 10
of
816