计算机科学
卷积神经网络
预处理器
任务(项目管理)
人工智能
特征(语言学)
动作(物理)
特征提取
民用航空
动作识别
热红外
航空
红外线的
班级(哲学)
工程类
语言学
哲学
物理
系统工程
量子力学
航空航天工程
光学
作者
Meng Ding,Yuanyuan Ding,Li Wei,Yiming Xu,Yunfeng Cao
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:53 (2): 1084-1094
被引量:3
标识
DOI:10.1109/tsmc.2022.3192017
摘要
The COVID-19 crisis has led to an unusually large number of commercial aircraft being currently parked or stored. For airlines, airports, and civil aviation authorities around the world, monitoring, and protecting these parked aircraft to prevent them from causing human-made damage are becoming urgent problems that are receiving increasing attention. In this study, we use thermal infrared monitoring videos to establish a framework for individual surveillance around parked aircraft by proposing a human action recognition (HAR) algorithm. As the focus of this article, the proposed HAR algorithm seamlessly integrates a preprocessing module in which a novel data structure is constructed to introduce spatiotemporal information of the action; a convolutional neural network-based module for spatial feature extraction; a triple-layer convolutional long short-term memory network for temporal feature extraction; and two fully connected layers for classification. Moreover, because no infrared dataset is available for the HAR task on airport grounds at nighttime, we present a dataset called IIAR-30, which consists of eight action categories that frequently occur on airport grounds and 2000 video clips. The experimental results on the IIAR-30 dataset demonstrated that the recognition accuracy of the proposed method was higher than 96%. We also further evaluated the effectiveness of the proposed method by comparing it with five baselines and four other methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI