Name
Abstract | ||
|---|---|---|
Distributed signal processing for sensor networks with node-specific interest requires the common labeling of all objects of interest. Current methods formulate the labeling task as a data clustering problem after extracting source-specific features. They assume perfect knowledge of the number of clusters, which is mostly unavailable and possibly time-varying. Thus, we propose distributed and adaptive Bayesian cluster enumeration algorithms by extending our recently proposed single node methods to a distributed sensor network setup where the nodes exchange information via the diffusion principle. The proposed methods are applied to a camera network use-case, where multiple users film a nonstationary scene from different angles. The number of pedestrians is estimated based on streaming-in feature vectors without assuming prior information, such as known positions of the devices, registration of camera views or the availability of a fusion center. Experimental results show the effectiveness of the proposed methods for synthetic and real data. |
Year | Venue | Field |
|---|---|---|
2018 | SSP | Signal processing,Feature vector,Bayesian information criterion,Pattern recognition,Computer science,Enumeration,Fusion center,Artificial intelligence,Cluster analysis,Wireless sensor network,Bayesian probability |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| freweyni k teklehaymanot | 1 | 8 | 3.51 |
| Michael Muma | 2 | 144 | 19.51 |
| Abdelhak M. Zoubir | 3 | 1036 | 148.03 |