17.1.4.4 Surveillance Systems for Smoke Detection, Aerial Image Smoke Detection

Chapter Contents (Back)
Smoke Detection. A lot of overlap in Forest fires:
See also Smoke from Forest Fires, Smoke from Wildfires.

Tseng, D.C.[Din-Chang], Shieh, W.S.[Wern-Sheng],
Plume extraction using entropic thresholding and region growing,
PR(26), No. 5, May 1993, pp. 805-817.
Elsevier DOI 0401
BibRef
Earlier:
Plume segmentation using local entropic thresholding,
ICPR92(III:156-159).
IEEE DOI 9208
Smoke plumes. BibRef

Yuan, F.N.[Fei-Niu],
A fast accumulative motion orientation model based on integral image for video smoke detection,
PRL(29), No. 7, 1 May 2008, pp. 925-932.
Elsevier DOI 0804
Video smoke detection; Fire detection; Motion estimation; Orientation accumulation BibRef

Calderara, S.[Simone], Piccinini, P.[Paolo], Cucchiara, R.[Rita],
Vision based smoke detection system using image energy and color information,
MVA(22), No. 4, July 2011, pp. 705-719.
WWW Link. 1107
BibRef
Earlier:
Smoke Detection in Video Surveillance: A MoG Model in the Wavelet Domain,
CVS08(xx-yy).
Springer DOI 0805
BibRef
Earlier: A2, A1, A3:
Reliable smoke detection in the domains of image energy and color,
ICIP08(1376-1379).
IEEE DOI 0810
BibRef

Vezzani, R.[Roberto], Calderara, S.[Simone], Piccinini, P.[Paolo], Cucchiara, R.[Rita],
Smoke detection in video surveillance: The use of ViSOR (video surveillance on-line repository),
CIVR08(289-298). 0807
BibRef

Zhao, T., Ackerman, S., Guo, W.,
Dust and Smoke Detection for Multi-Channel Imagers,
RS(2), No. 10, October 2010, pp. 2347-2368.
DOI Link 1203
BibRef

Verstockt, S.[Steven], Poppe, C.[Chris], van Hoecke, S.[Sofie], Hollemeersch, C.[Charles], Merci, B.[Bart], Sette, B.[Bart], Lambert, P.[Peter], van de Walle, R.[Rik],
Silhouette-based multi-sensor smoke detection: Coverage analysis of moving object silhouettes in thermal and visual registered images,
MVA(23), No. 6, November 2012, pp. 1243-1262.
WWW Link. 1210
BibRef

Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.,
Higher Order Linear Dynamical Systems for Smoke Detection in Video Surveillance Applications,
CirSysVideo(27), No. 5, May 2017, pp. 1143-1154.
IEEE DOI 1705
Analytical models, Classification algorithms, Dynamics, Fires, Focusing, Spatiotemporal phenomena, Wavelet analysis, Bag of systems, dynamic texture analysis, higher order decomposition, linear dynamical systems, smoke, detection BibRef

Yuan, F.N.[Fei-Niu], Shi, J.T.[Jin-Ting], Xia, X.[Xue], Zhang, L.[Lin], Li, S.Y.[Shu-Ying],
Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition,
CVIU(178), 2019, pp. 43-53.
Elsevier DOI 1812
Scale invariance, Rotation invariance, Local binary pattern, Pairwise comparing LBP, Smoke recognition BibRef

Yuan, F.N.[Fei-Niu], Shi, J.T.[Jin-Ting], Xia, X.[Xue], Huang, Q.H.[Qing-Hua], Li, X.L.[Xue-Long],
Co-occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition,
IET-CV(13), No. 2, March 2019, pp. 178-187.
DOI Link 1902
BibRef

Yuan, F.N.[Fei-Niu], Zhang, L.[Lin], Wan, B.Y.[Bo-Yang], Xia, X.[Xue], Shi, J.T.[Jin-Ting],
Convolutional Neural Networks Based on Multi-Scale Additive Merging Layers for Visual Smoke Recognition,
MVA(30), No. 2, March 2019, pp. 345-358.
WWW Link. 1904
BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Smoke vehicle detection based on robust codebook model and robust volume local binary count patterns,
IVC(86), 2019, pp. 17-27.
Elsevier DOI 1906
Smoke from vehicles. Polution detection. Smoke vehicle detection, Codebook model, Volume local binary count (VLBC), Non-redundant VLBC, Completed VLBC BibRef

Li, S.[Sen], Wang, S.Y.[Shu-Yan], Zhang, D.[Dan], Feng, C.Y.[Chun-Yong], Shi, L.[Long],
Real-time smoke removal for the surveillance images under fire scenario,
SIViP(13), No. 5, July 2019, pp. 1037-1043.
Springer DOI 1906
BibRef

Yuan, F.N.[Fei-Niu], Li, G.[Gang], Xia, X.[Xue], Lei, B.J.[Bang-Jun], Shi, J.T.[Jin-Ting],
Fusing texture, edge and line features for smoke recognition,
IET-IPR(13), No. 14, 12 December 2019, pp. 2805-2812.
DOI Link 1912
BibRef

Yuan, F., Zhang, L., Xia, X., Huang, Q., Li, X.,
A Wave-Shaped Deep Neural Network for Smoke Density Estimation,
IP(29), 2020, pp. 2301-2313.
IEEE DOI 2001
Estimation, Image segmentation, Feature extraction, Semantics, Image color analysis, Decoding, Visualization, Deep neural network, smoke simulation BibRef

Gu, K., Xia, Z., Qiao, J., Lin, W.,
Deep Dual-Channel Neural Network for Image-Based Smoke Detection,
MultMed(22), No. 2, February 2020, pp. 311-323.
IEEE DOI 2001
Feature extraction, Neural networks, Training, Deep learning, Convolutional codes, Convolution, Safety, Smoke detection, classification BibRef

Park, K.M.[Kyung-Min], Bae, C.O.[Cherl-O],
Smoke detection in ship engine rooms based on video images,
IET-IPR(14), No. 6, 11 May 2020, pp. 1141-1149.
DOI Link 2005
BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Smoke vehicle detection based on multi-feature fusion and hidden Markov model,
RealTimeIP(17), No. 3, June 2020, pp. 745-758.
Springer DOI 2006
BibRef

Li, Q.R.[Qing-Rong], Liu, H.[Hui], Zhang, J.P.[Jun-Peng], Zeng, P.F.[Peng-Fei],
Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm,
IET-IPR(14), No. 12, October 2020, pp. 2879-2889.
DOI Link 2010
BibRef

Tao, H.J.[Huan-Jie], Lu, X.B.[Xiao-Bo],
Smoke Vehicle Detection Based on Spatiotemporal Bag-Of-Features and Professional Convolutional Neural Network,
CirSysVideo(30), No. 10, October 2020, pp. 3301-3316.
IEEE DOI 2010
Feature extraction, Image color analysis, Vehicle detection, convolutional neural networks (CNN) BibRef

Chaturvedi, S.[Shubhangi], Khanna, P.[Pritee], Ojha, A.[Aparajita],
A survey on vision-based outdoor smoke detection techniques for environmental safety,
PandRS(185), 2022, pp. 158-187.
Elsevier DOI 2202
Outdoor smoke, Smoke detection, Smoke classification, Smoke segmentation, Smoke bounding box detection BibRef

Zhang, J.D.[Jie-Dong], Xie, W.H.[Wen-Hui], Liu, H.Y.[Hong-Yan], Dang, W.Y.[Wen-Yi], Yu, A.F.[An-Feng], Liu, D.[Di],
Compressed dual-channel neural network with application to image-based smoke detection,
IET-IPR(16), No. 4, 2022, pp. 1036-1043.
DOI Link 2203
BibRef

Cao, Y.C.[Yi-Chao], Tang, Q.[Qingfei], Wu, X.[Xuehui], Lu, X.B.[Xiao-Bo],
EFFNet: Enhanced Feature Foreground Network for Video Smoke Source Prediction and Detection,
CirSysVideo(32), No. 4, April 2022, pp. 1820-1833.
IEEE DOI 2204
Feature extraction, Task analysis, Monitoring, Dynamics, Shape, Convolutional neural networks, Feature foreground, source prediction BibRef

Yuan, F.N.[Fei-Niu], Dong, Z.[Zeshu], Zhang, L.[Lin], Xia, X.[Xue], Shi, J.[Jinting],
Cubic-cross convolutional attention and count prior embedding for smoke segmentation,
PR(131), 2022, pp. 108902.
Elsevier DOI 2208
Smoke segmentation, Information embedding, Cubic-cross convolutional attention, Count prior attention BibRef

Tao, H.[Huanjie], Duan, Q.[Qianyue],
Learning Discriminative Feature Representation for Estimating Smoke Density of Smoky Vehicle Rear,
ITS(23), No. 12, December 2022, pp. 23136-23147.
IEEE DOI 2212
Feature extraction, Semantics, Image segmentation, Estimation, Logic gates, Interference, Data mining, Smoke density, spatial and channel attention BibRef

Chen, X.[Xin], Xue, Y.P.[Yi-Peng], Zhu, Y.L.[Yao-Lin], Ma, R.Q.[Rui-Qing],
A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments,
IET-IPR(17), No. 7, 2023, pp. 1991-2004.
DOI Link 2305
computer vision, image recognition, object detection, object recognition BibRef

Jing, T.[Tao], Zeng, M.[Ming], Meng, Q.H.[Qing-Hao],
SmokePose: End-to-End Smoke Keypoint Detection,
CirSysVideo(33), No. 10, October 2023, pp. 5778-5789.
IEEE DOI 2310
BibRef

Li, R.G.[Rui-Guo], Wu, H.N.[Huai-Ning],
Multi-Robot Plume Source Localization by Distributed Quantum-Inspired Guidance With Formation Behavior,
ITS(24), No. 11, November 2023, pp. 11889-11904.
IEEE DOI 2311
BibRef

Zhang, L.[Lin], Wu, J.[Jing], Yuan, F.N.[Fei-Niu], Fang, Y.M.[Yu-Ming],
Smoke-Aware Global-Interactive Non-Local Network for Smoke Semantic Segmentation,
IP(33), 2024, pp. 1175-1187.
IEEE DOI 2402
Transformers, Semantics, Task analysis, Convolution, Feature extraction, Context modeling, Computational modeling, smoke-aware loss BibRef


Besbes, O.[Olfa], Benazza-Benyahia, A.[Amel],
Extracting Relevant Features from Videos for a Robust Smoke Detection,
ACIVS17(406-417).
Springer DOI 1712
BibRef

Tripathi, A.K.[Abhishek Kumar], Swarup, S.[Shanti],
Visual Smoke Detection,
NTIRE16(I: 128-142).
Springer DOI 1704
BibRef

Yamaguchi, S.[Shiori], Hirai, K.[Keita], Horiuchi, T.[Takahiko],
Video Smoke Removal Based on Smoke Imaging Model and Space-Time Pixel Compensation,
CCIW17(43-54).
Springer DOI 1704
BibRef

Chen, D.[Da], Li, W.B.[Wen-Bin], Hall, P.[Peter],
Dense Motion Estimation for Smoke,
ACCV16(IV: 225-239).
Springer DOI 1704
BibRef

Wattanachote, K., Li, K., Wang, Y., Shih, T.K., Liu, W.,
Preliminary Investigation on Stationarity of Dynamic Smoke Texture and Dynamic Fire Texture Based on Motion Coherent Metric,
CMVIT17(99-104)
IEEE DOI 1704
computer vision BibRef

Bombrun, M.[Maxime], Barra, V.[Vincent], Harris, A.[Andrew],
Analysis of Thermal Video for Coarse to Fine Particle Tracking in Volcanic Explosion Plumes,
SCIA15(366-376).
Springer DOI 1506
BibRef

Filonenko, A., Hernandez, D.C., Jo, K.H.[Kang-Hyun],
Smoke detection for static cameras,
FCV15(1-4)
IEEE DOI 1506
cameras BibRef

Meng, Z.Y.[Zhao-Yi], Koniges, A.[Alice], He, Y., Willianms, S., Kurth, T., Cook, B., Deslippe, J., Bertozzi, A.L.[Andrea L.],
OpenMP parallelization and optimization of graph-based machine learning algorithms,
LNCS(9903), Springer, 2016. In: OpenMP: Memory, Devices, and Tasks.
Springer DOI
See also Hyperspectral Image Classification Using Graph Clustering Methods. BibRef 0000

Hu, H.[Huiyi], Sunu, J.[Justin], Bertozzi, A.L.[Andrea L.],
Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme,
EMMCVPR15(209-222).
Springer DOI 1504

See also Hyperspectral Image Classification Using Graph Clustering Methods. BibRef

Merkurjev, E.[Ekaterina], Sunu, J.[Justin], Bertozzi, A.L.[Andrea L.],
Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video,
ICIP14(689-693)
IEEE DOI 1502
Chemical plumes. BibRef

He, H.Q.[Hai-Qian], Peng, L.Q.[Li-Qun], Yang, D.[Deshun], Chen, X.[Xiaoou],
Smoke Detection Based on a Semi-supervised Clustering Model,
MMMod14(II: 291-298).
Springer DOI 1405
BibRef

Pahalawatta, K.K., Green, R.,
Particle Detection and Classification in Photoelectric Smoke Detectors Using Image Histogram Features,
DICTA13(1-8)
IEEE DOI 1402
image representation BibRef

Chen, J.Q.[Jia-Qiu], Wang, Y.W.[Yao-Wei], Tian, Y.H.[Yong-Hong], Huang, T.J.[Tie-Jun],
Wavelet based smoke detection method with RGB Contrast-image and shape constrain,
VCIP13(1-6)
IEEE DOI 1402
edge detection BibRef

Wang, Y.[Yue], Chua, T.W.[Teck Wee], Chang, R.[Richard], Pham, N.T.[Nam Trung],
Real-time smoke detection using texture and color features,
ICPR12(1727-1730).
WWW Link. 1302
BibRef

Pahalawatta, K.K., Green, R.,
Classifying Airborne Particles,
DICTA11(376-381).
IEEE DOI 1205
BibRef
Earlier:
Nano-scale particle classification using image histogram maximum value index of Rayleigh scattered images,
IVCNZ10(1-6).
IEEE DOI 1203
e.g. dust and smoke. BibRef

Maruta, H.[Hidenori], Nakamura, A.[Akihiro], Yamamichi, T.[Takeshi], Kurokawa, F.[Fujio],
Image based smoke detection with local Hurst exponent,
ICIP10(4653-4656).
IEEE DOI 1009
BibRef

Wan, V.[Victoria], Braun, W.J., Dean, C.B.[Charmaine B.], Henderson, S.,
A comparison of classification algorithms for the identification of smoke plumes from satellite images,
CGC10(332).
PDF File. 1006
BibRef

Kopilovic, I., Vágvölgyi, B., Szirányi, T.,
Application of Panoramic Annular Lens for Motion Analysis Tasks: Surveillance and Smoke Detection,
ICPR00(Vol IV: 714-717).
IEEE DOI 0009
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Hardware, Sensor and Camera Arrangements for Surveillance Systems .


Last update:Mar 16, 2024 at 20:36:19