Name
Abstract | ||
|---|---|---|
The analysis of video footage regarding the identification of persons at defined locations or the detection of complex activities is still a challenging process. Nowadays, various (semi-)automated systems can be used to overcome different parts of these challenges. Object detection and their classification reach even higher detection rates when making use of the latest cutting-edge convolutional neural network frameworks. Integrated into a scalable infrastructure as a service data base system, we employ the combination of such networks by using the Detectron framework within Docker containers with case-specific engineered tracking and motion pattern heuristics in order to detect several activities with comparatively low and distributed computing efforts and reasonable results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WACVW.2019.00012 | 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) |
Keywords | DocType | ISSN |
Streaming media,Tracking,Task analysis,Object detection,Distributed databases,Cameras | Conference | 2572-4398 |
ISBN | Citations | PageRank |
978-1-7281-1392-0 | 1 | 0.41 |
References | Authors | |
0 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rico Thomanek | 1 | 1 | 5.48 |
| Christian Roschke | 2 | 1 | 6.16 |
| Benny Platte | 3 | 1 | 4.47 |
| Robert Manthey | 4 | 5 | 3.72 |
| Tony Rolletschke | 5 | 1 | 4.13 |
| Manuel Heinzig | 6 | 1 | 0.41 |
| Matthias Vodel | 7 | 1 | 0.41 |
| Frank Zimmer | 8 | 137 | 11.95 |
| Frank Zimmer | 9 | 137 | 11.95 |
| Maximilian Eibl | 10 | 119 | 37.66 |