| Paper: | TA-P4.9 |
| Session: | Video Object Segmentation and Tracking I |
| Time: | Tuesday, September 18, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
MEAN-SHIFT BLOB TRACKING WITH ADAPTIVE FEATURE SELECTION AND SCALE ADAPTATION |
| Authors: |
Dawei Liang; Harbin Institute of Technology | | |
| | Qingming Huang; Chinese Academy of Sciences | | |
| | Shuqiang Jiang; Chinese Academy of Sciences | | |
| | Hongxun Yao; Harbin Institute of Technology | | |
| | Wen Gao; Peking University | | |
| Abstract: |
When the appearances of the tracked object and surrounding background change during tracking, fixed feature space tends to cause tracking failure. To address this problem, we propose a method to embed adaptive feature selection into mean shift tracking framework. From a feature set, the most discriminative features are selected after ranking these features based on their Bayes error rates, which are estimated from object and background samples. For the selected features, a criterion is proposed to evaluate their stability for tracking and to guide feature reselection. The selected features are used to generate a weight image, in which mean shift is employed to locate the object. Moreover, a simple yet effective scale adaptation method is proposed to deal with object changing in size. Experiments on several video sequences show the effectiveness of the proposed method. |
