Loading...
Please wait, while we are loading the content...
Similar Documents
Classification-based likelihood functions for bayesian tracking.
| Content Provider | CiteSeerX |
|---|---|
| Author | Shen, Chunhua Li, Hongdong Brooks, Michael J. |
| Abstract | 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 and those that do not. This paper describes a general framework for learning probabilistic models of objects for exploiting these models for tracking objects in image sequences. We use a discriminative classifier to learn models of how they appear in images. In particular, we use a support vector machine (SVM) for training, which is able to extract useful non-linear information, and thus represent more complex characteristics of the tracked object and background. This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur. A by-product of the SVM training procedure is the classification function, with which the tracking problem is cast into a binary classification problem. An object detector directly using the classification function is then available. To make the tracker robust, an object detector that directly uses the classification function is combined into the tracker for object verification. This provides the capability for automatic initialisation and recovery from momentary tracking failures. We demonstrate improved robustness in image sequences. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Classification-based Likelihood Function Bayesian Tracking Classification Function Object Detector Image Sequence Improved Robustness Unstable Illumination Binary Classification Problem Tracking Problem Appearance Change Useful Non-linear Information Likelihood Function General Framework Complex Characteristic Pose Occur Automatic Initialisation Object Verification Tracked Object Bayesian Particle Deformable Object Discriminative Classifier Support Vector Machine Tracker Relies Probabilistic Model Momentary Tracking Failure Particular Advantage Tracker Robust Svm Training Procedure |
| Content Type | Text |
| Resource Type | Article |
