Deep learning-based human activity analysis for aerial images

Han Yang Wang, Ya Ching Chang, Yi Yu Hsieh, Hua Tsung Chen, Jen-Hui Chuang

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-of-the-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post-processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.

原文English
主出版物標題2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面713-718
頁數6
ISBN(電子)9781538621592
DOIs
出版狀態Published - 19 一月 2018
事件25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Xiamen, China
持續時間: 6 十一月 20179 十一月 2017

出版系列

名字2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
2018-January

Conference

Conference25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017
國家China
城市Xiamen
期間6/11/179/11/17

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