HUMAN DETECTION BASED ON A SEQUENCE OF THERMAL IMAGES USING DEEP LEARNING

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Hosseinyalamdary, S.^2¹  Wang, X.^1² 

[1] Department of Earth Observation Science (EOS), Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands^2;Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands^1

关键词: Human detection;    Temporal consistency;    Deep learning;    Thermal images;   

DOI  :  10.5194/isprs-archives-XLII-2-W13-127-2019

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY