期刊论文

Video Flame Detection Method Based on Target Tracking and Deep Learning

中文

计算机测量与控制

1671-4598

2019

27

7

159-163

10.16526/j.cnki.11-4762/tp.2019.07.035

湖北省自然科学基金(2017CFB591)。

本校为第一机构

近年来火灾事故频发,对生态环境,社会经济都造成了严重影响,视频监控系统在火灾预防和环境监控中都有非常重要的作用;针对传统的视频火焰检测方法需要手工提取火焰特征且识别率低、误检率高的缺点,提出了一种基于特征检测,多目标跟踪和深度学习的火焰检测算法;通过高斯混合模型运动检测方法对视频中的动态目标进行提取,再经过HSI与RGB结合的颜色模型进行筛选,得到疑似火焰目标,对提取的目标进行多目标跟踪算法跟踪,最终对稳定存在的目标通过CaffeNet模型进行判别,得到火焰判别结果;实验证明,本算法实现了对视频火焰的准确检测,能对火焰进行有效识别,对火焰视频数据集上的平均识别精度达到98.79%...

In recent years, frequent fire accidents have seriously affected the ecological environment and social economy,video surveillance system plays a very important role in fire prevention and environmental monitoring. To overcome the shortcomings of traditional video flame detection methods, such as manual extraction of flame features, low recognition rate and high false detection rate, a flame detection algorithm based on feature detection, multi-target tracking and deep learning is proposed. The algorithm extracts the dynamic object in video by the Gaussian mixture model motion detection method,...