Name
Abstract | ||
|---|---|---|
The public location of CCTV cameras and their connexion with public safety demand high robustness and reliability from surveillance systems. This paper focuses on the development of a multimodal fusion technique which exploits the benefits of a Bayesian inference scheme to enhance surveillance systems' reliability. Additionally, an automatic object classifier is proposed based on the multimodal fusion technique, addressing semantic indexing and classification for forensic applications. The proposed Bayesian-based Multimodal Fusion technique, and particularly, the proposed object classifier are evaluated against two state-of-the-art automatic object classifiers on the i-LIDS surveillance dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33885-4_47 | ECCV Workshops (3) |
Keywords | Field | DocType |
bayesian multimodal fusion,proposed bayesian-based multimodal fusion,public location,bayesian inference scheme,i-lids surveillance dataset,proposed object classifier,automatic object classifier,forensic application,surveillance system,public safety demand,state-of-the-art automatic object classifier,multimodal fusion technique | Data mining,Bayesian inference,Computer science,Search engine indexing,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Computer vision,Pattern recognition,Support vector machine,Exploit,Bayesian network,Bayesian probability | Conference |
Volume | ISSN | Citations |
7585 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virginia Fernandez Arguedas | 1 | 27 | 4.20 |
| Qianni Zhang | 2 | 113 | 24.17 |
| ebroul izquierdo | 3 | 1050 | 148.03 |