Name
Abstract | ||
|---|---|---|
Learning patterns of human movement is a complex and hard task, including several computationally expensive algorithms. This issue has even higher emphasis in mobile environment, since handheld devices contain significantly less memory and computing power than a usual PC does. In this paper we are going to compare novel, mobile-optimized methods for separating trajectories in a human routine recognition framework. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ECBS.2012.35 | ECBS |
Keywords | Field | DocType |
computationally expensive algorithm,mobile environment,handheld device,mobile-optimized method,usual pc,human routine recognition framework,route separation strategies,higher emphasis,computing power,human movement,hard task,human movement datasets,gps,algorithm,trajectory,global positioning system,shape,pattern,cybernetics | Computer vision,Character recognition,Computer science,Real-time computing,Mobile device,Human–computer interaction,Global Positioning System,Artificial intelligence,Trajectory,Cybernetics | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcell Feher | 1 | 3 | 2.29 |
| Krisztian Fekete | 2 | 1 | 1.02 |
| Kristof Csorba | 3 | 9 | 2.53 |
| Bertalan Forstner | 4 | 18 | 7.65 |