Name
Abstract | ||
|---|---|---|
The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/VCIP.2015.7457859 | 2015 Visual Communications and Image Processing (VCIP) |
Keywords | Field | DocType |
People localization,probabilistic occupancy map,video surveillance,multiple cameras,efficient algorithm | Computer vision,Data mining,Noise measurement,Probability estimation,Computer science,Occupancy,Artificial intelligence,Probabilistic logic,Computation complexity | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yen-Shuo Lin | 1 | 8 | 1.81 |
| Hua-Tsung Chen | 2 | 289 | 28.72 |
| Jen-Hui Chuang | 3 | 363 | 42.28 |