Miner detection method based on conditional random field
-
摘要: 针对现有视频图像目标检测算法应用于矿工检测时检出率、定位准确率、检测效率等均较低的问题,提出了一种基于条件随机场的矿工检测方法。该方法包括矿工检测模型建立与矿工检测识别2部分。在模型建立阶段,提取若干样本图像的方向梯度直方图特征,并利用主成分分析法对特征进行降维处理;以条件随机场为框架进行感兴趣区域标志,以标定训练样本,并训练条件随机场模型参数。在检测识别阶段,提取待检测图像的方向梯度直方图特征,并对特征进行降维,采用训练得到的条件随机场模型,通过局部二元模式推断标定图像各子窗口,最终得到矿工所在区域。实验结果表明,该方法可准确地检测出矿工在图像中的位置。Abstract: For low detection ratio, accuracy and efficiency of existing object detection methods of video image, a miner detection method based on conditional random field was proposed. The method mainly includes two sections, namely model foundation and recognition of miner detection. In the model foundation stage, characteristics of histogram of oriented gradient of sample images are extracted, whose dimensionalities are reduced by principal component analysis. Then interested regions are signed by conditional random field to calibrate training sample, and parameters of conditional random field model are trained. In the recognition stage, characteristics of histogram of oriented gradient of detected image are extracted, whose dimensionalities are reduced. The trained conditional random field model is used to calibrate each sub window of the detected image by local binary pattern method, so as to get region where miner is. The experimental results show that the method can detect miner in image correctly.
点击查看大图
计量
- 文章访问数: 24
- HTML全文浏览量: 5
- PDF下载量: 2
- 被引次数: 0