Name
Abstract | ||
|---|---|---|
Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1093/ietfec/e89-a.3.747 | IEICE Transactions |
Keywords | Field | DocType |
monocular color image sequence,background subtraction method,traffic monitoring,shadow bar,cast shadow,different vehicle,ycrcb color space,active cast shadow,general vehicle type classification,shadow detection | Background subtraction,Computer vision,Shadow,Color space,Monitoring system,Feature extraction,Artificial intelligence,Monocular,Mathematics,Cognitive neuroscience of visual object recognition,Color image | Journal |
Volume | Issue | ISSN |
E89-A | 3 | 0916-8508 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeong-Hoon Cho | 1 | 5 | 1.69 |
| Dae-Geun Jang | 2 | 25 | 5.14 |
| Chan-Sik Hwang | 3 | 5 | 3.02 |