基于连续密度隐马尔可夫模型的矿下异常行为识别算法

doi:10.13229/j.cnki.jdxbgxb20200160

摘要/Abstract

摘要：

针对当前方法识别精度不高的问题，提出了基于连续密度隐马尔可夫模型的矿下异常行为识别算法。获取待识别矿下视频帧数据，通过级联分类器实现运动区域的初步检测，并读入下帧数据，直到所有帧检测完毕。引入连续密度隐马尔可夫（HMM）模型，将人体图像分解成若干相等区域，获取图像区域中的标准差值特征，对连续密度HMM进行训练，完成异常行为识别。实验结果证明，本文算法的识别结果具有精度高和检测率高的特性，说明其具有可靠性。

关键词: 计算机应用, 连续密度, 马尔可夫模型, 矿下异常, 识别

Abstract:

In the current research of abnormal behaviors in underground mines the result accuracy needs improvement. In order to solve this problem and improve the safety of underground mines， a continuous density hidden Markov model is proposed to identify abnormal behaviors in underground mines. In the detection of human motion area under the mine， first， the video frame data to be identified is obtained； the human motion area is preliminarily extracted for each image， and the preliminary detection of human motion area is realized by cascading classifiers to get more accurate human motion area. Then the next frame data is read in， and this process is iterated until all frames are detected. According to the preliminary detection results， the continuous density hidden Markov model is introduced to decompose the human body image into several equal areas. The representative color features and the standard difference features in the image area are obtained. The continuous density HMM of the target is trained through the obtained feature data. The abnormal behavior recognition of the human body target under the mine is completed according to the training model. The experimental results show that the proposed algorithm has the characteristics of high accuracy and high detection rate in different number of human behavior recognition， which shows that the algorithm is reliable.

Key words: computer application, continuous density, Markov model, underground anomaly, identification

中图分类号:

TP39

王淑敏,陈伟. 基于连续密度隐马尔可夫模型的矿下异常行为识别算法[J]. 吉林大学学报(工学版), 2021, 51(3): 1067-1072.

Shu-min WANG,Wei CHEN. Algorithm for identifying abnormal behavior in underground mines based on continuous density hidden Markov model[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1067-1072.

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