Name
Abstract | ||
|---|---|---|
Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18082560 | SENSORS |
Keywords | Field | DocType |
vehicle counting,unmanned aerial vehicle,vehicle detection,visual tracking,aerial video | Computer vision,Vehicle counting,Electronic engineering,Vehicle detection,Artificial intelligence,Engineering | Journal |
Volume | Issue | Citations |
18 | 8.0 | 2 |
PageRank | References | Authors |
0.44 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-Zhi Xiang | 1 | 12 | 7.35 |
| Mingliang Zhai | 2 | 12 | 4.31 |
| Ning Lv | 3 | 31 | 11.32 |
| Abdulmotaleb El-Saddik | 4 | 2416 | 248.48 |