Fire locating method based on the convex hull algorithm using partially blocked surveillance cameras

WANG Guanning; CHEN Tao; MI Wenzhong; LIANG Xiaoliang; WANG Rudong

doi:10.16511/j.cnki.qhdxxb.2021.22.038

PDF(4105 KB)

Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (2) : 277-284. DOI: 10.16511/j.cnki.qhdxxb.2021.22.038

SPECIAL SECTION:DISASTER PREVENTION AND MITIGATION

Fire locating method based on the convex hull algorithm using partially blocked surveillance cameras

{{article.zuoZhe_EN}}

Author information +

History +

Abstract

Video analyses have become very useful in fire investigations. However, the cause of the fire cannot be easily determined when the surveillance cameras are partially blocked. This study analyzes fire locating methods based on the convex hull algorithm when the surveillance cameras are partially blocked. An oil pan pool fire is used to simulate an indoor fire. The charge-couple device (CCD) camera views are partially blocked with the fire scene videos then collected for analyses. The flame videos are collected and preprocessed before analyzing. Then, a convex hull algorithm is used to calculate the pixel coordinates of the flame center. Finally, the monocular vision principle is used to develop a matrix transformation between the world coordinate system and the imaging plane coordinate system to accurately determine the fire location. Tests show that the monocular visual positioning method based on the convex hull algorithm using partially blocked cameras can accurately locate the fire origin as a powerful tool for fire investigations.

Key words

convex hull / video analysis / monocular vision / fire investigation / fire locating

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Guanning, CHEN Tao, MI Wenzhong, LIANG Xiaoliang, WANG Rudong. Fire locating method based on the convex hull algorithm using partially blocked surveillance cameras[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(2): 277-284 https://doi.org/10.16511/j.cnki.qhdxxb.2021.22.038

References

[1] XU Z, MEI L, LIU Y H, et al. Semantic enhanced cloud environment for surveillance data management using video structural description[J]. Computing, 2016, 98(1):35-54.
[2] TANG S Y, SHU X M, HU J, et al. Online-offline associated surveillance system based on E-V fusion[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(6):576-580. (in Chinese)唐诗洋, 疏学明, 胡俊, 等. 基于E-V融合的线上-线下联合监控技术[J]. 清华大学学报(自然科学版), 2018, 58(6):576-580.
[3] LI D X, FEI G Y, TENG S W. Learning large margin multiple granularity features with an improved Siamese network for person re-identification[J]. Symmetry, 2020, 12(1):92.
[4] HUANG T G, SHARMA A. Technical and economic feasibility assessment of a cloud-enabled traffic video analysis framework[J]. Journal of Big Data Analytics in Transportation, 2020, 2(3):223-233.
[5] XING L, HE J, ABDEL-ATY M, et al. Time-varying analysis of traffic conflicts at the upstream approach of toll plaza[J]. Accident Analysis & Prevention, 2020, 141:105539.
[6] COSTA L R, RAUEN M S, FRONZA A B. Car speed estimation based on image scale factor[J]. Forensic Science International, 2020, 310:110229.
[7] CHEN C, BAI S, HUANG L D, et al. Research on monitoring and early-warning of passenger flow in crowded places based on video analysis[J]. Journal of Safety Science and Technology, 2020, 16(4):143-148. (in Chinese)陈冲, 白硕, 黄丽达, 等. 基于视频分析的人群密集场所客流监控预警研究[J]. 中国安全生产科学技术, 2020, 16(4):143-148.
[8] TAO Y N, SU F, YUAN P J, et al. Intelligent criminal investigation system based on both footprint recognition and surveillance video analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9):1730-1738. (in Chinese)陶一宁, 苏峰, 袁培江, 等. 联合足迹识别与监控视频分析的智能刑侦系统[J]. 北京航空航天大学学报, 2020, 46(9):1730-1738.
[9] National Fire Protection Association. Guide for fire and explosion investigations, 2017 edition:NFPA 921-2017[S]. Quincy, USA:National Fire Protection Association, 2017.
[10] LI Z H, SUN Y Y, XU L, et al. Explosion scene forensic image interpretation[J]. Journal of Forensic Sciences, 2019, 64(4):1221-1229.
[11] WANG G N, CHEN T, MI W Z, et al. Fire investigation method using completely blocked surveillance cameras[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(2):128-134. (in Chinese)王冠宁, 陈涛, 米文忠, 等. 监控完全遮挡场景下火灾调查方法[J]. 清华大学学报(自然科学版), 2021, 61(2):128-134.
[12] YU H Y, LI G R, ZHANG W G, et al. The unmanned aerial vehicle benchmark:Object detection, tracking and baseline[J]. International Journal of Computer Vision, 2020, 128(5):1141-1159.
[13] KAKANI V, NGUYEN V H, KUMAR B P, et al. A critical review on computer vision and artificial intelligence in food industry[J]. Journal of Agriculture and Food Research, 2020, 2:100033.
[14] TIAN H K, WANG T H, LIU Y D, et al. Computer vision technology in agricultural automation:A review[J]. Information Processing in Agriculture, 2020, 7(1):1-19.
[15] XU Y, DING C, SHU X K, et al. Shared control of a robotic arm using non-invasive brain-computer interface and computer vision guidance[J]. Robotics and Autonomous Systems, 2019, 115:121-129.
[16] WANG L. Study on fire location technology for fire truck based on image[D]. Xuzhou:China University of Mining and Technology, 2019. (in Chinese)王丽. 基于图像的消防车用火灾定位技术研究[D]. 徐州:中国矿业大学, 2019.
[17] LI L J, YANG H J. Revised detection and localization algorithm for Camellia oleifera fruits based on convex hull theory[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(12):285-292, 346. (in Chinese)李立君, 阳涵疆. 基于改进凸壳理论的遮挡油茶果定位检测算法[J]. 农业机械学报, 2016, 47(12):285-292, 346.
[18] CUPEC R, VIDOVIĆG I, FILKO D, et al. Object recognition based on convex hull alignment[J]. Pattern Recognition, 2020, 102:107199.
[19] AN P T, HUYEN P T T, LE N T. A modified Graham's convex hull algorithm for finding the connected orthogonal convex hull of a finite planar point set[J]. Applied Mathematics and Computation, 2021, 397:125889.
[20] CHEN K Y, ZOU X J, GUAN Z H, et al. Camera calibration method of picking robot based on shuffled frog leaping optimization[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(1):23-34. (in Chinese)陈科尹, 邹湘军, 关卓怀, 等. 基于混合蛙跳优化的采摘机器人相机标定方法[J]. 农业机械学报, 2019, 50(1):23-34.
[21] SONG H B, HE D J, PAN J P. Recognition and localization methods of occluded apples based on convex hull theory[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(22):174-180. (in Chinese)宋怀波, 何东健, 潘景朋. 基于凸壳理论的遮挡苹果目标识别与定位方法[J]. 农业工程学报, 2012, 28(22):174-180.