Name
Abstract | ||
|---|---|---|
Keeping a safe city against security breaches and acts of violence is something critical. In a smart video-surveillance system, 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 propose a model based-on the analysis of all the video surveillance data extracted from camera networks by exploiting the performance of the space-time covariance descriptor. This model not only deals with one video frame as the majority of models, but also considers all the extracted groups of pictures to implicitly encode the described pedestrian in motion by the integration of time parameter with the appearance features such as color, gradient and LBP, and the clustering step. The experiments conducted on PRID dataset showed the importance of video surveillance data analytics in recognition rates. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WETICE.2018.00046 | 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) |
Keywords | Field | DocType |
Multi-shot human re-identification,Space-time features,Covariance matrix,LBP operators,K-means clustering,PRID dataset | k-means clustering,Computer vision,ENCODE,Pedestrian,Data analysis,Computer science,Camera network,Artificial intelligence,Covariance matrix,Cluster analysis,Distributed computing,Covariance | Conference |
ISSN | ISBN | Citations |
1524-4547 | 978-1-5386-6917-4 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bassem Hadjkacem | 1 | 0 | 0.68 |
| Walid Ayedi | 2 | 28 | 5.10 |
| Mohamed Abid | 3 | 28 | 2.74 |
| Hichem Snoussi | 4 | 509 | 62.19 |