Early Wildfire Smoke Detection in Videos

被引:11
作者
Gupta, Taanya [1 ]
Liu, Hengyue [1 ]
Bhanu, Bir [1 ]
机构
[1] Univ Calif Riverside, Dept Elect & Comp Engn, Riverside, CA 92521 USA
来源
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2021年
关键词
BAG-OF-FEATURES; FIRE-DETECTION; FRAMEWORK; MOTION;
D O I
10.1109/ICPR48806.2021.9413231
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.
引用
收藏
页码:8523 / 8530
页数:8
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