Name
Abstract | ||
|---|---|---|
Shadow detection and removal is important to deal with traffic image sequences. The shadow cast by a vehicle can lead to inaccurate object feature extraction and an erroneous scene analysis. Furthermore, separate vehicles can be connected through a shadow, thereby confusing an object recognition system. Accordingly, this paper proposes a robust method for detecting and removing an active cast shadow from monocular color image sequences. A background subtraction method is used to extract moving blobs in color and gradient dimensions, and YCrCb color space adopted to detect and remove the cast shadow. Even when shadows link different vehicles, each vehicle figure can be separately detected using a modified mask based on a shadow bar. Experimental results from town scenes demonstrate that the proposed method is effective and the classification accuracy is sufficient for general vehicle type classification. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_77 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
monocular color image sequence,shadow cast,background subtraction method,cast shadow detection,shadow bar,cast shadow,different vehicle,ycrcb color space,active cast shadow,visual traffic surveillance,general vehicle type classification,shadow detection,background subtraction,object recognition,color image,feature extraction,color space | Background subtraction,Monocular vision,Computer vision,Shadow,Color space,Computer science,Image processing,Feature extraction,Artificial intelligence,Cognitive neuroscience of visual object recognition,Color image | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
2 | 0.53 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeong-Hoon Cho | 1 | 5 | 1.69 |
| Tae-Gyun Kwon | 2 | 2 | 0.53 |
| Dae-Geun Jang | 3 | 25 | 5.14 |
| Chan-Sik Hwang | 4 | 5 | 3.02 |