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Pedestrian Detection in Unmanned Aerial Vehicle Scene

  • Qianqian Guo
  • Yihao Li
  • Dong WangEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

With the increasing adoption of unmanned aerial vehicles (UAVs), pedestrian detection with use of such vehicles has been attracting attention. Object detection algorithms based on deep learning have considerably progressed in recent years, but applying existing research results directly to the UAV perspective is difficult. Therefore, in this study, we present a new dataset called UAVs-Pedestrian, which contains various scenes and angles, for improving test results. To validate our dataset, we use the classical detection algorithms SSD, YOLO, and Faster-RCNN. Findings indicate that our dataset is challenging and conducive to the study of pedestrian detection using UAVs.

Keywords

Pedestrian detection UAVs-Pedestrian dataset Deep learning 

Notes

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities (No. DUT18JC30) and Undergraduate Innovation and Entrepreneurship Training Program (No. 2018101410201011075).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina

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