Abstract
Human detection has been playing an increasingly important role in many fields in recent years. Human detection is a still challenging task because, for the group of people, each individual has his unique appearance and, body shape. Compared with the traditional method, the deep learning neural network has the advantages of shorter computing time, higher accuracy and easier operation. Therefore, deep learning method has been widely used in object detection. The current state of art in human detection is RetinaNet. Among all the deep learning approaches, RetinaNet gives the highest accuracy of human detection (Lin, Goyal, Girshick, He, & Piotr Dollar, 2018).The temporal component of video provides additional and significant clues as compared to the static image. In this paper, the temporal relationship of the images is utilized to improve the accuracy of human detection. Compared to using only an image, the accuracy of human detection is 21.4% higher when a sequence of images is applied. .
Original language | English |
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Title of host publication | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Editors | G. Vosselman, S.J. Oude Elberink, M.Y. Yang |
Publisher | International Society for Photogrammetry and Remote Sensing (ISPRS) |
Pages | 127-132 |
Number of pages | 6 |
Volume | 42 |
Edition | 2/W13 |
DOIs | |
Publication status | Published - 4 Jun 2019 |
Event | 4th ISPRS Geospatial Week 2019 - University of Twente, Enschede, Netherlands Duration: 10 Jun 2019 → 14 Jun 2019 Conference number: 4 https://www.gsw2019.org/ |
Publication series
Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
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Publisher | Copernicus |
ISSN (Print) | 1682-1750 |
Conference
Conference | 4th ISPRS Geospatial Week 2019 |
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Country/Territory | Netherlands |
City | Enschede |
Period | 10/06/19 → 14/06/19 |
Internet address |
Keywords
- Deep learning
- Human detection
- Temporal consistency
- Thermal images
- ITC-GOLD