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Proceedings Paper

Using infrared HOG-based pedestrian detection for outdoor autonomous searching UAV with embedded system
Author(s): Yanhua Shao; Yanying Mei; Hongyu Chu; Zhiyuan Chang; Yuxuan He; Huayi Zhan
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Paper Abstract

Pedestrian detection (PD) is an important application domain in computer vision and pattern recognition. Unmanned Aerial Vehicles (UAVs) have become a major field of research in recent years. In this paper, an algorithm for a robust pedestrian detection method based on the combination of the infrared HOG (IR-HOG) feature and SVM is proposed for highly complex outdoor scenarios on the basis of airborne IR image sequences from UAV. The basic flow of our application operation is as follows. Firstly, the thermal infrared imager (TAU2-336), which was installed on our Outdoor Autonomous Searching (OAS) UAV, is used for taking pictures of the designated outdoor area. Secondly, image sequences collecting and processing were accomplished by using high-performance embedded system with Samsung ODROID-XU4 and Ubuntu as the core and operating system respectively, and IR-HOG features were extracted. Finally, the SVM is used to train the pedestrian classifier. Experiment show that, our method shows promising results under complex conditions including strong noise corruption, partial occlusion etc.

Paper Details

Date Published: 10 April 2018
PDF: 5 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153Z (10 April 2018); doi: 10.1117/12.2304530
Show Author Affiliations
Yanhua Shao, Southwest Univ. of Science and Technology (China)
Yanying Mei, Southwest Univ. of Science and Technology (China)
Hongyu Chu, Southwest Univ. of Science and Technology (China)
Zhiyuan Chang, Southwest Univ. of Science and Technology (China)
Yuxuan He, Southwest Univ. of Science and Technology (China)
Huayi Zhan, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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