Name
Abstract | ||
|---|---|---|
We present a novel approach for real-time and proactive navigation in crowded environments such as event spaces and urban areas where many people are moving to their destinations simultaneously. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. Our approach tries to detect future congestion by using a spatio-temporal statistical method that predicts people flow. When future congestion is detected, our approach creates an optimal navigation plan based on \"what-if\" simulations, which accounts for the effect of total people flow change caused by navigation. We experimentally compare the spatio-temporal statistical method with the conventional matrix factorization based approach using a real data set. We also demonstrate the effectiveness of our navigation approach by computer simulation using artificial people-flow data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2800835.2801624 | UbiComp/ISWC Adjunct |
Field | DocType | Citations |
Computer vision,Computer science,Navigation system,Matrix decomposition,Artificial intelligence,Mobile robot navigation | Conference | 3 |
PageRank | References | Authors |
0.40 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naonori Ueda | 1 | 1902 | 214.32 |
| Futoshi Naya | 2 | 144 | 19.12 |
| Hitoshi Shimizu | 3 | 4 | 3.13 |
| Tomoharu Iwata | 4 | 824 | 65.87 |
| Maya Okawa | 5 | 10 | 3.23 |
| Hiroshi Sawada | 6 | 1809 | 136.96 |