Name
Abstract | ||
|---|---|---|
The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/AVSS.2019.8909830 | 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Keywords | Field | DocType |
Drone detection,Deep learning,Faster R-CNN | Computer vision,Computer science,Artificial intelligence,Drone | Conference |
ISSN | ISBN | Citations |
2643-6205 | 978-1-7281-0991-6 | 1 |
PageRank | References | Authors |
0.38 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mrunalini Nalamati | 1 | 1 | 0.38 |
| Ankit Kapoor | 2 | 1 | 0.38 |
| Muhammed Saqib | 3 | 1 | 0.38 |
| Nabin Sharma | 4 | 132 | 11.55 |
| M. Blumenstein | 5 | 168 | 31.87 |