Most cited papers in Fire Detection and Safety Systems
What are the most cited papers in Fire Detection and Safety Systems?
The most cited paper in Fire Detection and Safety Systems is "Fully-Convolutional Siamese Networks for Object Tracking" (Luca Bertinetto, Jack Valmadre, João F. Henriques et al., 2016), with 4,443 citations. Next come "High Performance Visual Tracking with Siamese Region Proposal Network" (3,035) and "GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild" (1,860).
Showing 50 of 100 papers. Every paper with more than 288 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Fully-Convolutional Siamese Networks for Object Tracking Luca Bertinetto, Jack Valmadre, João F. Henriques et al. · Lecture notes in computer science | 2016 | 4,443 |
| 2 | High Performance Visual Tracking with Siamese Region Proposal Network Bo Hu Li, Junjie Yan, Wei Wu et al. | Jun 2018 | 3,035 |
| 3 | GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild Lianghua Huang, Xin Bo Zhao, Kaiqi Huang · IEEE Transactions on Pattern Analysis and Machine Intelligence | 4 Dec 2019 | 1,860 |
| 4 | An Enhanced Contextual Fire Detection Algorithm for MODIS Louis Giglio, Jacques Descloitres, Christopher O. Justice et al. · Remote Sensing of Environment | Oct 2003 | 1,855 |
| 5 | Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4) Louis Giglio, James T. Randerson, Guido R. van der Werf · Journal of Geophysical Research Biogeosciences | Mar 2013 | 1,770 |
| 6 | LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking Heng Fan, Liting Lin, Fan Yang et al. | Jun 2019 | 1,712 |
| 7 | Distractor-Aware Siamese Networks for Visual Object Tracking Zheng Dong Zhu, Qiang Wang, Bo Li et al. · Lecture notes in computer science | 2018 | 1,542 |
| 8 | Real-time foreground–background segmentation using codebook model Kyungnam Kim, Thanarat H. Chalidabhongse, David Harwood et al. · Real-Time Imaging | 4 Apr 2005 | 1,429 |
| 9 | Deep Joint Rain Detection and Removal from a Single Image Wenhan Yang, Robby T. Tan, Jiashi Feng et al. | Jul 2017 | 1,282 |
| 10 | Cross-scene crowd counting via deep convolutional neural networks Cong Zhang, Hongsheng Li, Xiaogang Wang et al. | Jun 2015 | 1,193 |
| 11 | Simple rainflow counting algorithms Stephen Downing, D. F. Socie · International Journal of Fatigue | 1982 | 1,157 |
| 12 | Towards Real-Time Multi-Object Tracking Zhongdao Wang, Liang Zheng, Yixuan Liu et al. · Lecture notes in computer science | 2020 | 1,103 |
| 13 | Switching Convolutional Neural Network for Crowd Counting Deepak Babu Sam, Shiv Surya, R. Venkatesh Babu | Jul 2017 | 1,019 |
| 14 | Remote sensing techniques to assess active fire characteristics and post-fire effects Leigh B. Lentile, Zachary A. Holden, Alistair M. S. Smith et al. · International Journal of Wildland Fire | 5 Sep 2006 | 974 |
| 15 | Heat release rate: The single most important variable in fire hazard Vytenis Babrauskas, Richard D. Peacock · Fire Safety Journal | 1992 | 906 |
| 16 | MixFormer: End-to-End Tracking with Iterative Mixed Attention Yutao Cui, Cheng Jiang, Limin Wang et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 841 |
| 17 | Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework Botao Ye, Hong Chang, Bingpeng Ma et al. · Lecture notes in computer science | 2022 | 791 |
| 18 | Scale Aggregation Network for Accurate and Efficient Crowd Counting Xinkun Cao, Zhipeng Wang, Yanyun Zhao et al. · Lecture notes in computer science | 2018 | 783 |
| 19 | A review of machine learning applications in wildfire science and management Piyush Jain, Sean C.P. Coogan, Sriram Ganapathi Subramanian et al. · Environmental Reviews | 28 Jul 2020 | 761 |
| 20 | The Sixth Visual Object Tracking VOT2018 Challenge Results Matej Kristan et al. · Lecture notes in computer science | 2019 | 713 |
| 21 | Robust techniques for background subtraction in urban traffic video Sen-ching S. Cheung, Chandrika Kamath · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE | 7 Jan 2004 | 679 |
| 22 | Triplet Loss in Siamese Network for Object Tracking Xingping Dong, Jianbing Shen · Lecture notes in computer science | 2018 | 667 |
| 23 | A Twofold Siamese Network for Real-Time Object Tracking Anfeng He, Chong Luo, Xinmei Tian et al. | Jun 2018 | 665 |
| 24 | A Review of Computer Vision Techniques for the Analysis of Urban Traffic Norbert Buch, Sergio A. Velastín, James Orwell · IEEE Transactions on Intelligent Transportation Systems | 17 Mar 2011 | 656 |
| 25 | An early fire-detection method based on image processing Thou-Ho Chen, Ping-Hsueh Wu, Yung-Chuen Chiou | 19 Apr 2005 | 649 |
| 26 | Computer vision based method for real-time fire and flame detection Behçet Uğur Töreyın, Yiğithan Dedeoğlu, Uğur Güdükbay et al. · Pattern Recognition Letters | 27 Aug 2005 | 641 |
| 27 | A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques Chi Yuan, Youmin M. Zhang, Zhixiang Liu · Canadian Journal of Forest Research | 12 Mar 2015 | 638 |
| 28 | An improved fire detection approach based on YOLO-v8 for smart cities Fatma M. Talaat, Hanaa ZainEldin · Neural Computing and Applications | 28 Jul 2023 | 636 |
| 29 | Historical background and current developments for mapping burned area from satellite Earth observation Emilio Chuvieco, Florent Mouillot, Guido R. van der Werf et al. · Remote Sensing of Environment | 5 Mar 2019 | 632 |
| 30 | Coal fires burning out of control around the world: thermodynamic recipe for environmental catastrophe Glenn Blair Stracher, Tammy P. Taylor · International Journal of Coal Geology | 9 Mar 2004 | 620 |
| 31 | Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking Qiang Wang, Teng Fang Zhu, Junliang Xing et al. | Jun 2018 | 619 |
| 32 | A zigbee-based home automation system Khusvinder Gill, Shuang‐Hua Yang, Fang Qin Yao et al. · IEEE Transactions on Consumer Electronics | May 2009 | 615 |
| 33 | Large Margin Object Tracking with Circulant Feature Maps Mengmeng Wang, Yong Liu, Zeyi Huang | Jul 2017 | 607 |
| 34 | VITAL: VIsual Tracking via Adversarial Learning Yibing Song, Chao Ma, Xiaohe Wu et al. | Jun 2018 | 594 |
| 35 | A survey on gas leak detection and localization techniques Pal-Stefan Murvay, Ioan Silea · Journal of Loss Prevention in the Process Industries | 29 May 2012 | 568 |
| 36 | A Forest Fire Detection System Based on Ensemble Learning Renjie Xu, Haifeng Lin, Kangjie Lu et al. · Forests | 13 Feb 2021 | 563 |
| 37 | Early fire detection using convolutional neural networks during surveillance for effective disaster management Khan Muhammad, Jamil Ahmad, Sung Wook Baik · Neurocomputing | 29 Dec 2017 | 559 |
| 38 | Convolutional Neural Networks Based Fire Detection in Surveillance Videos Khan Muhammad, Jamil Ahmad, Irfan Mehmood et al. · IEEE Access | 2018 | 554 |
| 39 | Fire detection in video sequences using a generic color model Turgay Çelik, Hasan Demirel · Fire Safety Journal | 8 Jul 2008 | 554 |
| 40 | Local Fisher Discriminant Analysis for Pedestrian Re-identification Sateesh Pedagadi, James Orwell, Sergio A. Velastín et al. | Jun 2013 | 550 |
| 41 | A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing Panagiotis Barmpoutis, Periklis Papaioannou, Kosmas Dimitropoulos et al. · Sensors | 11 Nov 2020 | 543 |
| 42 | Computer vision and deep learning techniques for pedestrian detection and tracking: A survey Antonio Brunetti, Domenico Buongiorno, Gianpaolo Francesco Trotta et al. · Neurocomputing | 11 Mar 2018 | 542 |
| 43 | Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications Khan Muhammad, Jamil Ahmad, Zhihan Lv et al. · IEEE Transactions on Systems Man and Cybernetics Systems | 14 Jun 2018 | 536 |
| 44 | Robust Object Tracking by Hierarchical Association of Detection Responses Chang Huang, Bo Wu, Ramakant Nevatia · Lecture notes in computer science | 2008 | 510 |
| 45 | Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking Heng Fan, Haibin Ling | Jun 2019 | 491 |
| 46 | Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion Pasquale Foggia, Alessia Saggese, Mario Vento · IEEE Transactions on Circuits and Systems for Video Technology | 19 Jan 2015 | 481 |
| 47 | Electrostatic precipitation Akira Mizuno · IEEE Transactions on Dielectrics and Electrical Insulation | 2000 | 466 |
| 48 | Real-time forest fire detection with wireless sensor networks Liyang Yu, Neng Wang, Xiaoqiao Meng | 10 Dec 2005 | 465 |
| 49 | SeqTrack: Sequence to Sequence Learning for Visual Object Tracking Xin Chen, Houwen Peng, Dong Wang et al. | Jun 2023 | 462 |
| 50 | Intelligent video surveillance: a review through deep learning techniques for crowd analysis G. Sreenu, M.A. Saleem Durai · Journal Of Big Data | 6 Jun 2019 | 460 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.