Name
Abstract | ||
|---|---|---|
Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TIP.2010.2050644 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
motion detection algorithm,variational energy,motion alarm,detection failure,digital video recorder,robust motion detection,segmentation,motion detection,proposed motion detection model,closed circuit television,motion detection sensitivity level,image sequences,closed circuit television camera,video surveillance system,environmentally robust motion detection,energy minimization,video surveillance systems,preselected motion sensitivity level,robust detection method,video surveillance,image motion analysis,mathematical model,noise,embedded system,image segmentation,pixel | Computer vision,Signal processing,Motion detection,Computer science,Segmentation,Digital signal,Image segmentation,Artificial intelligence,Pixel,Constant false alarm rate,Closed-circuit television camera | Journal |
Volume | Issue | ISSN |
19 | 11 | 1941-0042 |
Citations | PageRank | References |
3 | 0.50 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyenkyun Woo | 1 | 160 | 8.43 |
| Yoon Mo Jung | 2 | 58 | 6.09 |
| Jeong-Gyoo Kim | 3 | 4 | 0.85 |
| Jin Keun Seo | 4 | 376 | 58.65 |