基于视频的煤矿井下人员目标检测与跟踪研究
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摘要
煤矿井下存在一些危险区域,这些区域禁止人员进入,为保障安全,需要有效的人员检测手段。目前,煤矿企业绝大多数配备了视频监控系统,若通过视频检测人员,进而实现报警和联动控制,对于煤矿安全生产具有重要意义。
由于井下环境特殊,全天候人工照明,导致煤矿井下视频光照不均匀,缺乏色彩信息,目标与背景相似,甚至干扰比目标还要突出,给运动目标的自动检测带来很大困难,也使得目前的检测方法直接应用于井下视频检测效果不理想。本文针对煤矿井下视频,研究人员目标的检测和跟踪方法,从危险区域的视频图像中检测出人员目标并予以跟踪定位,为后续跟踪和行为分析奠定基础。主要工作如下:
针对井下视频图像照度低、光照不均匀的现象,提出基于模糊理论的井下视频增强算法。结合人类视觉系统感知信息时具有模糊性的特点,构造线性模糊化函数,进行模糊增强,实现了低亮度区域的增强,高亮度区域的抑制,并利用局部特性调整对比度,避免因模糊增强带来的对比度下降问题。实验结果表明,增强效果良好。
针对传统方法对于井下视频中目标检测效果不理想的现状,提出井下人员目标的模糊检测方法。结合时空信息,把差分图像和帧图像进行线性模糊;根据井下视频的特点,定义模糊规则,确定目标区域的隶属函数;结合人眼视觉特性,定义影团的概念和隶属函数,与前述的目标区域的隶属函数相结合,实现人员目标的检测;并把模糊检测和利用高斯混合模型对背景建模的方法结合起来,以适应动态背景下检测需求。实验结果表明:这种检测方法可以克服矿灯光对目标检测的影响,在目标与背景灰度相似的情况下也能够很好的检测到矿工;且没有复杂的运算,计算快速,能满足实际应用的实时要求。
针对煤矿井下视频中检测到的目标可能不完整或畸变,不利于判断目标是否为人员目标的情形,提出基于安全帽检测的井下人员检测方法。通过检测安全帽,可以在检测到目标的同时,直接表明目标为人员目标,克服井下视频中目标与背景相似对人员检测的影响,同时满足视频中人员目标非全身直立情况下的检测需求。通过对安全帽进行建模,获取安全帽图像,提取四方向边缘特征,用高斯函数模拟特征分布,采用线性分段函数区分视频中窗口为安全帽区域和非安全帽区域实现安全帽的检测。
提出了基于联合边缘方向和边缘方位信息直方图的Kalman-Meanshift安全帽跟踪方法。采用基于Kalman滤波、Mean-shift跟踪的方案,选择联合边缘方向和方位信息的直方图作为特征量,利用安全帽呈现类圆形的特性选择核函数带宽,实现了视频中安全帽的实时准确跟踪。
There are some dangerous regions in the underground coal mine, the miners are not allowed into these regions. Considering the requirements for the safety production of coal mine, the methods of human detection and tracking in underground coal mine videos are studied in this paper. The main research work includes:
A fuzzy enhancement method was proposed to overcome the impact of low-illumination and uneven lighting. A linear fuzzification function was constructed; and the image was enhanced fuzzily to enhance dark regions and to restrain the glaring regions; then, the contrast of the image enhanced was adjusted. The experimental results show that the method produces better result.
A fuzzy miner detection method was proposed. The difference image and the frame image were fuzzificated. Some fuzzy rulers according to the characters of coal mine videos were defined to get every pixel’s membership value in the object. The silhouette and its membership function were defined based on human vision. Two functions were combined to detect the miner object. On the other hand, the fuzzy detection was combined with the mixture Gaussian model to detect the miners in dynamic scene. The experimental results show that this method can remove the interference of miner’s lamp and detect the miner effectively even they are similar to the background. This method has the characteristics of less calculation and high velocity; it is suited for practical use in underground coal mines.
A method for detecting miners based on helmets detection was proposed. If a helmet is detected, it means that a miner is detected. The method constructed the standard images of helmets, extracted the four directional features, modeled the distribution of features using Gaussian function, designed piecewise linear classification, and separated the local image of frames into helmet and non-helmet. The experimental results show that this method can detect the helmets effectively.
Helmet tracking method was proposed. The tracker adopted Kalman-Meanshift scheme, constructed joint histogram based on edge orientation and position information, selected Kernel-bandwidth automaticly according to the oval shape of helmet, and tracked helmet accurately and real-timely.
由于井下环境特殊,全天候人工照明,导致煤矿井下视频光照不均匀,缺乏色彩信息,目标与背景相似,甚至干扰比目标还要突出,给运动目标的自动检测带来很大困难,也使得目前的检测方法直接应用于井下视频检测效果不理想。本文针对煤矿井下视频,研究人员目标的检测和跟踪方法,从危险区域的视频图像中检测出人员目标并予以跟踪定位,为后续跟踪和行为分析奠定基础。主要工作如下:
针对井下视频图像照度低、光照不均匀的现象,提出基于模糊理论的井下视频增强算法。结合人类视觉系统感知信息时具有模糊性的特点,构造线性模糊化函数,进行模糊增强,实现了低亮度区域的增强,高亮度区域的抑制,并利用局部特性调整对比度,避免因模糊增强带来的对比度下降问题。实验结果表明,增强效果良好。
针对传统方法对于井下视频中目标检测效果不理想的现状,提出井下人员目标的模糊检测方法。结合时空信息,把差分图像和帧图像进行线性模糊;根据井下视频的特点,定义模糊规则,确定目标区域的隶属函数;结合人眼视觉特性,定义影团的概念和隶属函数,与前述的目标区域的隶属函数相结合,实现人员目标的检测;并把模糊检测和利用高斯混合模型对背景建模的方法结合起来,以适应动态背景下检测需求。实验结果表明:这种检测方法可以克服矿灯光对目标检测的影响,在目标与背景灰度相似的情况下也能够很好的检测到矿工;且没有复杂的运算,计算快速,能满足实际应用的实时要求。
针对煤矿井下视频中检测到的目标可能不完整或畸变,不利于判断目标是否为人员目标的情形,提出基于安全帽检测的井下人员检测方法。通过检测安全帽,可以在检测到目标的同时,直接表明目标为人员目标,克服井下视频中目标与背景相似对人员检测的影响,同时满足视频中人员目标非全身直立情况下的检测需求。通过对安全帽进行建模,获取安全帽图像,提取四方向边缘特征,用高斯函数模拟特征分布,采用线性分段函数区分视频中窗口为安全帽区域和非安全帽区域实现安全帽的检测。
提出了基于联合边缘方向和边缘方位信息直方图的Kalman-Meanshift安全帽跟踪方法。采用基于Kalman滤波、Mean-shift跟踪的方案,选择联合边缘方向和方位信息的直方图作为特征量,利用安全帽呈现类圆形的特性选择核函数带宽,实现了视频中安全帽的实时准确跟踪。
There are some dangerous regions in the underground coal mine, the miners are not allowed into these regions. Considering the requirements for the safety production of coal mine, the methods of human detection and tracking in underground coal mine videos are studied in this paper. The main research work includes:
A fuzzy enhancement method was proposed to overcome the impact of low-illumination and uneven lighting. A linear fuzzification function was constructed; and the image was enhanced fuzzily to enhance dark regions and to restrain the glaring regions; then, the contrast of the image enhanced was adjusted. The experimental results show that the method produces better result.
A fuzzy miner detection method was proposed. The difference image and the frame image were fuzzificated. Some fuzzy rulers according to the characters of coal mine videos were defined to get every pixel’s membership value in the object. The silhouette and its membership function were defined based on human vision. Two functions were combined to detect the miner object. On the other hand, the fuzzy detection was combined with the mixture Gaussian model to detect the miners in dynamic scene. The experimental results show that this method can remove the interference of miner’s lamp and detect the miner effectively even they are similar to the background. This method has the characteristics of less calculation and high velocity; it is suited for practical use in underground coal mines.
A method for detecting miners based on helmets detection was proposed. If a helmet is detected, it means that a miner is detected. The method constructed the standard images of helmets, extracted the four directional features, modeled the distribution of features using Gaussian function, designed piecewise linear classification, and separated the local image of frames into helmet and non-helmet. The experimental results show that this method can detect the helmets effectively.
Helmet tracking method was proposed. The tracker adopted Kalman-Meanshift scheme, constructed joint histogram based on edge orientation and position information, selected Kernel-bandwidth automaticly according to the oval shape of helmet, and tracked helmet accurately and real-timely.
引文
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