Name
Abstract | ||
|---|---|---|
Wide-area wireless camera networks are being increasingly deployed in many urban scenarios. The large amount of data generated from these cameras pose significant information processing challenges. In this work, we focus on representation, search and retrieval of moving objects in the scene, with emphasis on local camera node video analysis. We develop a graph model that captures the relationships among objects without the need to identify global trajectories. Specifically, two types of edges are defined in the graph: object edges linking the same object across the whole network and context edges linking different objects within a spatial-temporal proximity. We propose a manifold ranking method with a greedy diversification step to order the relevant items based on similarity as well as diversity within the database. Detailed experimental results using video data from a 10-camera network covering bike paths are presented. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TMM.2013.2281019 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
video signal processing,image representation,distributed camera network,diverse and relevant ranking,graph-based modeling,moving object retrieval,manifold ranking method,moving object search,greedy diversification step,local camera node video analysis,global trajectory identification,graph-based topic-focused retrieval,image sensors,greedy algorithms,spatial-temporal proximity,image retrieval,moving object representation,edge detection,wide-area wireless camera networks,information processing challenges,graph theory,information search and retrieval | Graph theory,Computer vision,Graph,Information processing,Image sensor,Pattern recognition,Edge detection,Computer science,Camera auto-calibration,Image retrieval,Greedy algorithm,Artificial intelligence | Journal |
Volume | Issue | ISSN |
15 | 8 | 1520-9210 |
Citations | PageRank | References |
5 | 0.47 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiejun Xu | 1 | 147 | 11.11 |
| Vignesh Jagadeesh | 2 | 217 | 12.74 |
| Zefeng Ni | 3 | 85 | 8.07 |
| Santhoshkumar Sunderrajan | 4 | 41 | 4.89 |
| B. S. Manjunath | 5 | 20 | 1.85 |