Visual tracking method of shearer based on compressive sensing
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摘要: 针对工作面光照强度低且不均匀、煤尘浓度大等问题，提出了一种采煤机视频压缩感知跟踪方法。该方法首先采用矩形滤波器对图像进行归一化处理，获取特征向量；然后依据压缩感知理论对目标样本和背景样本的Haar-like特征向量进行压缩处理，并基于压缩后的Haar-like特征向量建立目标模型并训练朴素贝叶斯分类器；最后采用朴素贝叶斯分类器识别目标图像和背景图像，实现采煤机动态跟踪。试验结果表明，该方法在采煤机移动、遮挡及环境照度不均匀、快速变化等情况下都能实现有效跟踪，平均跟踪帧速率达22 帧/s。Abstract: For problems of low illumination intensity, uneven illumination and high coal dust concentration in working face, a visual tracking method of shearer based on compressive sensing was proposed. The image is normalized by use of rectangular filter firstly to get feature vectors. Then compressed Haar-like feature vectors of target samples and background samples are gotten according to compressive sensing theory for building target model and training naive Bayes classifier. The target image and background image are identified by the naive Bayes classifier finally, so as to realize dynamic tracking of shearer. The experimental result shows that the method can track shearer effectively when the shearer is moving or covered in environment of uneven and varied illumination, and average tracking frame rate is 22 frames per second.
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Keywords:
- coal mining /
- shearer /
- visual tracking /
- dynamic tracking /
- compressive sensing /
- feature extractio
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期刊类型引用(2)
1. 朱墨然. 煤矿瓦斯抽采钻孔三维模拟及控制效果评价. 矿业安全与环保. 2024(03): 50-55+64 . 
百度学术
2. 李泉新，姚克，方俊，姚亚峰，史春宝. 煤矿井下瓦斯高效精准抽采定向钻进技术与装备. 煤炭科学技术. 2023(S1): 65-72 . 百度学术
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