 |
Technical Program
Paper Detail
| Paper: | WA-L6.3 |
| Session: | Knowledge-Based Image Processing For Classification And Recognition In Surveillance Applications |
| Time: | Wednesday, October 11, 10:20 - 10:40 |
| Presentation: |
Special Session Lecture
|
| Title: |
KNOWLEDGE-BASED SUPERVISED LEARNING METHODS IN A CLASSICAL PROBLEM OF VIDEO OBJECT TRACKING |
| Authors: |
Lionel Carminati; LaBRI CNRS | | | | | Jenny Benois-Pineau; LaBRI CNRS | | | | | Christian Jennewein; LaBRI CNRS | | |
| Abstract: |
In this paper we present a new scheme for detection and tracking of specific objects in a knowledge-based framework. The scheme uses a supervised learning method: Support Vector Machines. Both problems, detection and tracking, are solved by a common approach: objects are located in video sequences by a SVM classifier. They are then tracked along the time by a SVM tracker with complete 6 parameters affine model. The method is applied in a video surveillance environment for detection and tracking of frontal view faces. Real time application constraints are met by reduction of support vector set. |
|
