The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier
Authors:
- 20 March 2018
- journal article
- research article
- Published by Springer Nature in Cluster Computing
- Vol. 22 (S3) , 7665-7675
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61772087)
- National Natural Science Foundation of China (61702052)
- the Science and Technology Service Platform of Hunan Province (2012TP1001)
- the Open Research Fund of Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (2015TP1005)
- the Changsha Science and Technology Planning (KQ1703018)
- The Research Foundation of Education Bureau of Hunan Province (12C0010, 17A007)
- the ZOOMLION Intelligent Technology Limited Company (2017zkhx130)
This publication has 11 references indexed in Scilit:
- A novel online incremental and decremental learning algorithm based on variable support vector machineCluster Computing, 2018
- Robotic Room-Level Localization Using Multiple Sets of Sonar MeasurementsIEEE Transactions on Instrumentation and Measurement, 2016
- Fault-tolerant multi-path routing protocol for WSN based on HEEDInternational Journal of Sensor Networks, 2016
- Computer Vision Based Real-time Fire Detection MethodJournal of Information and Computational Science, 2015
- Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire DetectionIEEE Transactions on Circuits and Systems for Video Technology, 2014
- Video Saliency Detection Using Contrast of Spatiotemporal Directional CoherenceIEEE Signal Processing Letters, 2014
- The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking FieldComputational and Mathematical Methods in Medicine, 2013
- Fire detection based on vision sensor and support vector machinesFire Safety Journal, 2009
- Fire detection using statistical color model in video sequencesJournal of Visual Communication and Image Representation, 2007
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004