Forest Fire Detection Based on Video Multi-Feature Fusion

被引:0
|
作者
Jie, Li [1 ]
Jiang, Xiao [1 ]
机构
[1] Beijing Forestry Univ, Sch Tecnol, Beijing, Peoples R China
关键词
forest fire detection; computer vision; fire detection; fire character; image process; image segmentation; sample;
D O I
10.1109/ICCSIT.2009.5234862
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the light of the problem of monitoring forest fire, the design strategy and practical implementation of establishing the monitor system based on digital image information are proposed. The system is based on the CCD configuration characteristics and color information to detect and locate fire. Manned lookout posts are commonly installed in the forests all around the world. In this project, a system capable of producing automatic fire alarms will be developed. It will be also possible to approximately determine the location of the fire and monitor the forest fires using wireless communications systems. The aim of the proposed system is to reduce the average fire detection rate and reduce the number of guards. This approach will reduce false alarms due to natural events. The experimental results show that it can effectively identify fire.
引用
收藏
页码:19 / 22
页数:4
相关论文
共 50 条
  • [21] Video Captioning based on Multi-feature Fusion with Object
    Zhou, Lijuan
    Liu, Tao
    Niu, Changyong
    THIRTEENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2021), 2021, 11878
  • [22] Smoke root detection from video sequences based on multi-feature fusion
    Lou, Liming
    Chen, Feng
    Cheng, Pengle
    Huang, Ying
    JOURNAL OF FORESTRY RESEARCH, 2022, 33 (06) : 1841 - 1856
  • [23] Video Flame Detection Based on Multi-feature Fusion and Double-layer XGBoost
    Wang, Yuanbin
    Li, Yujie
    Wu, Huaying
    Duan, Yu
    ENGINEERING LETTERS, 2022, 30 (02) : 904 - 911
  • [24] High-precision video flame detection algorithm based on multi-feature fusion
    Wang, Ying
    Li, Wen-Hui
    Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2010, 40 (03): : 769 - 775
  • [25] High Speed Front-Vehicle Detection Based on Video Multi-feature Fusion
    Xiong, Liliang
    Yue, Wenjing
    Xu, Qiushi
    Zhu, Zhengtian
    Chen, Zhi
    PROCEEDINGS OF 2020 IEEE 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2020), 2020, : 348 - 351
  • [26] Lightweight Deepfake Detection Based on Multi-Feature Fusion
    Yasir, Siddiqui Muhammad
    Kim, Hyun
    APPLIED SCIENCES-BASEL, 2025, 15 (04):
  • [27] Foreign Object Detection for Electric Energy Meters Based on Multi-feature Fusion and Random Forest
    Jiang, Xiaoyong
    Yang, Tao
    2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2019, : 1581 - 1584
  • [28] Multi-Feature Based Visual Saliency Detection in Surveillance Video
    Tong, Yubing
    Konik, Hubert
    Cheikh, Faouzi Alaya
    Guraya, Fahad Fazal Elahi
    Tremeau, Alain
    VISUAL COMMUNICATIONS AND IMAGE PROCESSING 2010, 2010, 7744
  • [29] Estimation of Ankle Angle Based on Multi-Feature Fusion with Random Forest
    Chen Huihui
    Gao Farong
    Chen Chao
    Tian Taixing
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 5549 - 5553
  • [30] Ship trajectory anomaly detection based on multi-feature fusion
    Huang, Guanbin
    Lai, Shanyan
    Ye, Chunyang
    Zhou, Hui
    2021 IEEE INTERNATIONAL CONFERENCE ON SMART DATA SERVICES (SMDS 2021), 2021, : 72 - 81