Moving target tracking and recognition method for unmanned airborne surveillance systems
This paper considers vision-based multiple moving target-tracking and target-type recognition methods for unmanned airborne surveillance systems. The detection of moving objects and target-type recognition in a moving image frame are the essential parts of airborne surveillance systems. We propose an optical flow-based object detection method with image stabilization functions to detect moving objects in a moving image frame, and a combination of the Support Vector Machine (SVM) with Convolutional Neural Networks (CNNs) model for the target-type recognition. The experiment of an airborne surveillance scenario using a quadcopter with a camera is conducted to demonstrate the performance of the proposed method. © ICROS 2017.
- Issue Date
- 2017
- Language
- Korean
- Article Type
- Article
- Keywords
Clutter (information theory); Convolution; Image processing; Monitoring; Neural networks; Object detection; Optical flows; Security systems; Stabilization; Support vector machines; Tracking (position); Airborne Surveillance; Convolutional neural network; Detection of moving object; Image stabilization; Moving target tracking; Quadcopter; SVM(support vector machine); Target type; Target tracking
- Citation
Journal of Institute of Control, Robotics and Systems, v.23, no.3, pp.157 - 164
- ISSN
- 1976-5622
- Appears in Collection
- AE-Journal Papers(저널논문)
- Files in This Item
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