Name
Abstract | ||
|---|---|---|
In this paper, we describe the design and implementation of a computationally efficient system for detecting moving objects on a moving platform which can be deployed on small, lightweight, low-cost and power-efficient hardware. The primary application of the payload system is that of performing real-time on-board autonomous object detection of moving objects from videos stream taken from a camera mounted to an unmanned aerial vehicle (UAV). The implemented object detection algorithms utilise recursive density estimation and evolving local means clustering to perform change and object detection of moving objects without prior knowledge. Furthermore, experiments are presented which demonstrate that the introduced system is able to detect, by on-board processing, any moving objects from a UAV in real time while at the same time sending only important data to a control station located on the ground with minimal time to set up and become operational. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s00521-016-2315-7 | Neural Computing and Applications |
Keywords | Field | DocType |
Autonomous objects detection, Unmanned aerial vehicle, Evolving clustering, Video analytic, Linear motion model | Density estimation,Object detection,Computer vision,Artificial intelligence,Analytics,Cluster analysis,Recursion,Mathematics,Payload | Journal |
Volume | Issue | ISSN |
28 | 5 | 1433-3058 |
Citations | PageRank | References |
2 | 0.36 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Plamen Angelov | 1 | 954 | 67.44 |
| Pouria Sadeghi-Tehran | 2 | 65 | 6.26 |
| Christopher Clarke | 3 | 21 | 2.75 |