Name
Abstract | ||
|---|---|---|
Detecting dangerous situations is crucial for emergency management. Surveillance systems detect dangerous situations by analyzing crowd dynamics. This paper presents a holistic video-based approach for privacy-preserving crowd density estimation. Our experimental approach leverages distributed, on-board pre-processing, allowing privacy as well as the use of low-power, low-throughput wireless communications to interconnect cameras. We developed a multicamera grid-based people counting algorithm which provides the density per cell for an overall view on the monitored area. This view comes from a merger of infrared and Kinect camera data. We describe our approach using a layered model for data aggregation and abstraction together with a workflow model for the involved software components, focusing on their functionality. The power of our approach is illustrated through the real-world experiment that we carried out at the Schönefeld airport in the city of Berlin. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2835596.2835603 | EM-GIS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmanuel Baccelli | 1 | 0 | 1.69 |
| Alexandra Danilkina | 2 | 0 | 0.34 |
| Sebastian Müller | 3 | 63 | 13.40 |
| Agnès Voisard | 4 | 327 | 62.99 |
| Matthias Wählisch | 5 | 54 | 17.86 |