Name
Abstract | ||
|---|---|---|
We present a method that allows autonomous systems to detect anomalous events in human-populated environments through understating of their structure and how they change over time. We represent the environment by temporary warped space-hypertime continuous models derived from patterns of changes driven by human activities within the observed space. The ability of the method to detect anomalies is evaluated on real-world datasets gathered by robots over the course of several weeks. An earlier version of this approach was already applied to robots that patrolled offices of a global security company (G4S). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-14984-0_5 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer science,Patrolling,Real-time computing,International security,Autonomous system (Internet),Robot | Conference | 11472 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomas Vintr | 1 | 7 | 3.63 |
| Kerem Eyisoy | 2 | 1 | 0.35 |
| Vanda Vintrová | 3 | 1 | 0.35 |
| Zhi Yan | 4 | 2 | 0.72 |
| Yassine Ruichek | 5 | 198 | 45.38 |
| Tomás Krajník | 6 | 422 | 37.83 |