标题:
基于计算机视觉的火车钩挡识别Train Interval Recognition Based on Computer Vision
作者:
单勇, 程环环
关键字:
计算机视觉, 视频处理, 特征提取, 特征融合, 火车钩档识别Computer Vision, Video Processing, Feature Extraction, Feature Fusion, Train Interval Recognition
期刊名称:
《Hans Journal of Data Mining》, Vol.5 No.2, 2015-04-09
摘要:
为了给运煤车喷洒抑尘剂，需要识别火车车厢之间的钩铛。基于计算机视觉的火车钩铛识别只需在站台一侧安装摄像头，对拍摄到的图像序列进行处理从而识别出钩铛。由于光照、天气等诸多因素的影响，首先采用直方图均衡算法对图像进行增强，然后提取图像的LBP纹理特征和直线特征，并进行特征融合，选用SVM (Support Vector Machine)分类器，针对不同的目标通过离线训练的方式获得SVM模型参数。针对四类目标训练了相应的SVM模型，分别为车头、车厢、钩挡和车站，对拍摄的运煤车视频图像可以实现在线识别。经过初步实验验证，提出的钩铛识别系统能够自动识别火车钩铛的开始和结束，从而控制喷洒系统，且安装简单，能够最大程度的减少抑尘剂浪费。Train intervals between carriages need to be recognized when we sprinkle the dust-restrainer on coal in the train. A train interval recognition method based on computer vision technology is pro-posed, which will set a video camera on one side of the platform to recognize the train intervals by processing image sequences. Because of the variation of illumination and weather, images are first enhanced by histogram equalization algorithm, and then image features including texture features and line features are extracted and integrated. The SVM model will be trained offline for four targets, including locomotive, carriage, interval and platform, and used to recognize the train in-tervals online. Our experimentations show that our system is capable of recognizing the beginning and the ending of train intervals automatically. Furthermore, the proposed system can be embeded in the sprinkle system easily to reduce the waste of dust-restrainer dramatically.