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