Name
Abstract | ||
|---|---|---|
With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/AVSS.2017.8078557 | 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Keywords | Field | DocType |
object proposal detectors,flying object detection,automatic UAV recognition,automatic UAV detection systems,unmanned aerial vehicles,security services,video imagery,video data,static cameras,moving cameras,image differencing,convolutional neural network,CNN | Computer vision,Object detection,Pattern recognition,Object-class detection,Computer science,Clutter,Convolutional neural network,Image differencing,Artificial intelligence,Detector | Conference |
ISBN | Citations | PageRank |
978-1-5386-2940-6 | 2 | 0.45 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lars Wilko Sommer | 1 | 31 | 9.49 |
| Arne Schumann | 2 | 85 | 14.01 |
| Thomas Muller | 3 | 2 | 0.45 |
| Tobias Schuchert | 4 | 93 | 12.21 |
| Jürgen Beyerer | 5 | 315 | 75.37 |