Abstract：Due to the low illumination, low contrast and similar color between target and environment in a coal mine, problems of undetected objects and false detections appear. An improved miner target detection method was proposed, integrating Gaussian Mixture Model (GMM) with Local Binary Pattern (LBP). The color information of background was fitted by means of GMM, and the texture information was extracted by employing LBP, then the miners targets were detected by integrating the color and the texture information. The simulation results indicate that the proposed algorithm decreases the problems of undetected objects and false detections, and can detect miner target in real-time with high precision.
鲜晓东 李克文. 基于颜色和纹理特征的伪装色矿工目标检测[J]. 计算机应用, 2013, 33(02): 539-542.
XIAN Xiaodong LI Kewen. Detection of camouflaged miner objects based on color and texture features. Journal of Computer Applications, 2013, 33(02): 539-542.
2013, Vol. 33 
