基于PCA-SIFT的煤矿监控目标识别及行为分析.doc

基于PCA-SIFT的煤矿监控目标识别及行为分析.doc基于PCA-SIFT的煤矿监控目标识别及行为分析
阚宝朋
淮安信息职业技术学院
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摘    要:
运动目标轮廓识别是提升煤矿井下监控预测价值的基础, 也是监控视频系统的开发难点。通过提出PCA-SIFT算法, 运用该算法对煤矿监控运动目标进行识别, 并将识别结果与传统Mean Shift算法对比。结果表明:PCA-SIFT算法可更加清晰地识别出井下图像轮廓, 其帧处理效率和正确率更高, 且运动目标跟踪误差十分稳定, 可有效防止跟踪目标丢失。
关键词:
井下监控; PCA-SIFT算法; 目标识别;
作者简介:阚宝朋(1982-) , 山东临沂人, 讲师, 硕士, 研究方向:软件工程、人工智能.
收稿日期:2017-04-23
Coal Mine Monitoring Target Recognition and Behavior Analysis Based on PCA-SIFT
KAN Bao-peng
Huaian College of Information Technology;
Abstract:
Moving object contour recognition is the basis of improving the value of monitoring and forecasting in coal mine, and it is also the difficulty of monitoring video the proposed PCA-SIFT algorithm, the algorithm is used to identify
the moving target of coal mine monitoring, and the recognition results pared with the traditional Mean Shift results show that the PCA-SIFT algorithm can be used to identify the image contour more clearly, the frame processing efficiency and accuracy are higher, and the tracking error of the moving object is very stable, which can effectively prevent the tracking target from losing.
Keyword:
underground monitoring; PCA-SIFT algorithm; object recognition;
Received: 2017-04-23
0 引言
煤矿监控是确保煤矿安全作业的基础工作, 其监控目标除了机车和矿井结构外, 还包括人、带式输送机等运动目标。通过有效监控, 一旦出现异常, 须及时发出警报并采取应对措施。从煤矿运行