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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.
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References
Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision, pp. 354–370 (2016)
Girshick, R.: Fast R-CNN. IEEE Int. Conf. Comput. Vis. 1440–1448 (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell.39(6), 1137 (2017)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. Eur. Conf. Comput. Vis. 21–37 (2016)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. IEEE Conf. Comput. Vis. Pattern Recognit. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. IEEE Conf. Comput. Vis. Pattern Recognit. 6517–6525 (2017)
Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis.88(2), 303–338 (2010)
Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick, C.L.: Microsoft COCO: common objects in context. Eur. Conf. Comput. Vis. 740–755, (2014)
Redmon, J., FarhadiRedmon, A.: YOLOv3: an incremental improvement. IEEE Conf. Comput. Vis. Pattern Recognit. (2018)
Wang, L.: Places205-VGGNet models for scene recognition. IEEE Conf. Comput. Vis. Pattern Recognit. 1135–1155 (2015)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot multibox detector. Eur. Conf. Comput. Vis. 21–37 (2016)
Huang, J., Rathod, V., Sun, C., Zhu, M., Balan, A.K., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., Murphy, K.: Speed/Accuracy Trade-offs for Modern Convolutional Object Detectors (2016).arXiv:1611.10012
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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School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China
Qianqian Guo, Yihao Li & Dong Wang
- Qianqian Guo
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- Yihao Li
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- Dong Wang
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Correspondence toDong Wang.
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Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Huimin Lu
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Guo, Q., Li, Y., Wang, D. (2020). Pedestrian Detection in Unmanned Aerial Vehicle Scene. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_26
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