当前位置: X-MOL 学术Pattern Recogn. › 论文详情
Human activity recognition from UAV-captured video sequences
Pattern Recognition ( IF 5.898 ) Pub Date : 2019-11-29 , DOI: 10.1016/j.patcog.2019.107140
Hazar Mliki; Fatma Bouhlel; Mohamed Hammami

This research paper introduces a new approach for human activity recognition from UAV-captured video sequences. The proposed approach involves two phases: an offline phase and an inference phase. A scene stabilization step is performed together with these two phases. The offline phase aims to generate the human/non-human model as well as a human activity model using a convolutional neural network. The inference phase makes use of the already generated models in order to detect humans and recognize their activities. Our main contribution lies in adapting the convolutional neural networks, normally dedicated to the classification task, to detect humans. In addition, the classification of human activities is carried out according to two scenarios: An instant classification of video frames and an entire classification of the video sequences. Relying on an experimental evaluation of the proposed methods for human detection and human activity classification on the UCF-ARG dataset, we validated not only these contributions but also the performance of our methods compared to the existing ones.
更新日期:2020-01-04

 

全部期刊列表>>
化学/材料学中国作者研究精选
Springer Nature 2019高下载量文章和章节
《科学报告》最新环境科学研究
ACS材料视界
自然科研论文编辑服务
中南大学国家杰青杨华明
剑桥大学-
中国科学院大学化学科学学院
材料化学和生物传感方向博士后招聘
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug