Name
Abstract | ||
|---|---|---|
Illumination changes present challenging problems to video surveillance algorithms tasked with identifying and tracking objects. Illumination changes can drastically alter the appearance of a scene, causing truly salient features to be lost amid otherwise stable background. We describe an illumination change compensation method that identifies large, stable, chromatically distinct background features-called BigBackground regions — which are used as calibration anchors for scene correction. The benefits of this method are demonstrated for a computationally low-cost kinematic tracking application as it attempts to track objects during illumination changes. The BigBackground-based method is compared with other compensation techniques, and is found to successfully track 60% to 80% more objects during illumination changes. Video sequences of pedestrian and vehicular traffic are used for evaluation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/WACV.2011.5711536 | Applications of Computer Vision |
Keywords | DocType | ISSN |
feature extraction,lighting,object detection,road traffic,traffic engineering computing,video signal processing,BigBackground region feature,illumination change compensation technique,kinematic tracking application,pedestrian video sequence,scene correction,vehicular traffic video sequence,video surveillance algorithms | Conference | 1550-5790 |
ISBN | Citations | PageRank |
978-1-4244-9496-5 | 1 | 0.35 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ryan Bales | 1 | 1 | 0.35 |
| Dana Forsthoefel | 2 | 11 | 2.18 |
| D. Scott Wills | 3 | 197 | 24.57 |
| Linda M Wills | 4 | 293 | 40.95 |