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Content Provider | IET Digital Library |
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Author | Wu, Qingtian Zhou, Yimin Wu, Xinyu Liang, Guoyuan Ou, Yongsheng Sun, Tianfu |
Abstract | A fast-running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real-time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior-processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small-kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real-time. |
Starting Page | 278 |
Ending Page | 287 |
Page Count | 10 |
ISSN | 1751956X |
Volume Number | 14 |
e-ISSN | 17519578 |
Issue Number | Issue 5, May (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-its/14/5 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0455 |
Journal | IET Intelligent Transport Systems |
Publisher Date | 2020-02-24 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | 81.1% Detection Accuracy Aerospace Control Autonomous Aerial Vehicle Complex Outdoor UAV Scenes Computer Vision And Image Processing Technique Convolutional Filter Deep Convolution Network Deep Convolutional Network Deep Learning Frameworks Feature Extraction Human Detection System Image Classification Image Recognition Image Sequence Knowledge Engineering Technique Learning in AI Mobile Robots Object Detection Optical Flow Optical, Image And Video Signal Processing Outer Contour Extraction Prior-processing Operation Remotely Operated Vehicle Running People Small-kernel Convolution Network Spatial Average Filtering Successive Frames Time Running Detection System UAV Imagery UAV Videos Unmanned Aerial Vehicle |
Content Type | Text |
Resource Type | Article |
Subject | Law Transportation Environmental Science Mechanical Engineering |
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