Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents an empirical study, towards a general and solid approach to fast detect fire and flame in videos, with the applications in video surveillance and event retrieval. Our system consists of three cascaded steps: (1) candidate regions proposing by a background model, (2) fire region classifying with color-texture features and a dictionary of visual words, and (3) temporal verifying. The experimental evaluation and analysis are done for each step. We believe that it is a useful service to both academic research and real-world application. In addition, we release the software of the proposed system with the source code, as well as a public benchmark and data set, including 64 video clips covered both indoor and outdoor scenes under different conditions. We achieve an 82% Recall with 93% Precision on the data set, and greatly improve the performance by state-of-the-arts methods.
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
Tecnologico de Monterrey（蒙特雷理工学院）；Universidad de las Américas Puebla（普埃布拉美洲大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；CIATEQ A.C.（CIATEQ先进技术中心）
FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management
Holcombe Department of Electrical and Computer Engineering, Clemson University（克莱姆森大学霍尔科姆电气与计算机工程系）；Pacific Southwest Research Station, U.S. Forest Services（美国林业局太平洋西南研究站）；School of Environmental and Forest Sciences, University of Washington（华盛顿大学环境与森林科学学院）；US Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory（美国林业局落基山研究站火灾科学实验室）；Department of Mechanical Engineering, Clemson University（克莱姆森大学机械工程系）；Department of Forest Management, University of Montana（蒙大拿大学森林管理系）
Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
Center for Applied Statistics, School of Statistics, Renmin University of China（应用统计中心，统计学院，中国人民大学）；Graduate School of Informatics, Kyoto University（信息研究生院，京都大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（社会人工智能与机器人研究所，深圳）；International Research Institute of Disaster Science, Tohoku University（灾害科学国际研究所，东北大学）
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Department of Electrical and Computer Engineering University of Illinois Chicago（电气与计算机工程系伊利诺伊大学芝加哥分校）；Fire and Rescue Department of Malaysia（马来西亚消防与救援部门）；Dept. of Chemical and Environmental Engineering Faculty of Engineering Universiti Putra Malaysia（化学与环境工程系乌拉尔大学马来西亚分校）；International Tropical Forestry Programme Faculty of Tropical Forestry Universiti Malaysia Sabah（国际热带林业计划热带林业系马来西亚沙巴大学）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）
A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification
Department of Electrical and Computer Engineering, University of Illinois Chicago（伊利诺伊大学芝加哥分校电子与计算机工程系）；Informatics Institute, Istanbul Technical University（伊斯坦布尔技术大学信息学院）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）；Department of Computer Engineering, Bilkent University（比尔肯特大学计算机工程系）
HydroSense: A Dual-Microcontroller IoT Framework for Real-Time Multi-Parameter Water Quality Monitoring with Edge Processing and Cloud Analytics
University of Frontier Technology, Bangladesh（前沿技术大学，孟加拉国）；Robo Tech Valley, Dhaka, Bangladesh（机器人技术谷，达卡，孟加拉国）；Bangalore University, Bangalore, India（班加罗尔大学，班加罗尔，印度）
