標題: | Learning a Scene Background Model via Classification |
作者: | Lin, Horng-Horng Liu, Tyng-Luh Chuang, Jen-Hui 資訊工程學系 Department of Computer Science |
關鍵字: | Background modeling;boosting;classification;tracking;SVM |
公開日期: | 1-五月-2009 |
摘要: | Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determine which image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included. |
URI: | http://dx.doi.org/10.1109/TSP.2009.2014810 http://hdl.handle.net/11536/7264 |
ISSN: | 1053-587X |
DOI: | 10.1109/TSP.2009.2014810 |
期刊: | IEEE TRANSACTIONS ON SIGNAL PROCESSING |
Volume: | 57 |
Issue: | 5 |
起始頁: | 1641 |
結束頁: | 1654 |
顯示於類別: | 期刊論文 |