A recognition method of abnormal patterns for video surveillance in unmanned substation

In order to improve the intelligent level of monitoring and timely detect abnormalities,an identification method of abnormal patterns of surveillance video in unmanned substation environment was proposed in this paper.Object classification was implemented in substation environment video surveillance(related to people,animals,ordinary flames(red and yellow flames),white flames,incandescent lamps).Multiple features were extracted.Hierarchical classifier structure was generated from the confusion matrix.Support vector machine(SVM) was used as the basic binary classifier.AdaBoost algorithm applies weighted voting on SVM whose classification accuracy was not ideal.Simulation experiment using actual video data was implemented,and experimental results show that the proposed method can get a better recognition for people,animals and flame and eliminate interferences such as the impact of incandescent lamps.It can provide the necessary conditions for fire alarm and steal precaution in unattended substation.