Name
Abstract | ||
|---|---|---|
Multi-shot human re-identification is a major challenge because of the large variations in a human's appearance caused by different types of noise such as occlusion, viewpoint and illumination variations. In this paper, we presented a novel Gabor-LBP based video covariance descriptor, called GL-VC descriptor, which considers image sequences to extract appearance features, captures moving regions of interest and find the correlation between video frames. Therefore, it implicitly encodes the described human motion by the integration of temporal information and decreases the effect of occlusion. To deal with the changes of view points and illumination, the Local binary pattern (LBP) operators and Gabor bank were integrated into the spatio-temporal covariance features. We evaluated our GL-VC approach on the publicly available CAVIAR4REID multi-shot dataset and demonstrated superior performance in comparison with the current state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-52941-7_20 | PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS 2016) |
Keywords | Field | DocType |
Multi-shot human re-identification,Spatio-temporel features,Covariance matrix,Gabor,LBP,CAVIAR4REID dataset | Computer vision,GLOH,Computer science,Local binary patterns,Human motion,Correlation,Operator (computer programming),Artificial intelligence,Covariance matrix,Covariance | Conference |
Volume | ISSN | Citations |
552 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bassem Hadjkacem | 1 | 3 | 1.07 |
| Walid Ayedi | 2 | 28 | 5.10 |
| Abid Mohamed | 3 | 39 | 19.08 |