Transformer substation personnel behavior recognition method based on monitoring video time sequence action positioning and anomaly detection
A monitoring video and anomaly detection technology, applied in character and pattern recognition, neural learning methods, instruments, etc., can solve problems that are difficult to be applied to identify staff, achieve accurate monitoring video sequence action positioning, accurately detect abnormal behavior, and improve Effect of Exploitation Value and Anomaly Detection Accuracy
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[0081] example,
[0082] like figure 1 shown.
[0083] A substation personnel behavior identification method based on monitoring video sequence action location and anomaly detection, the method comprises the following steps:
[0084] S1: Use prior knowledge to autonomously collect, process and construct a video dataset for monitoring abnormal behavior of substation personnel;
[0085] S2: Construct a 3D convolution feature extraction network: extract features from the input unsegmented substation monitoring long video, and extract the feature information of the monitoring video sequence;
[0086] S3: Build a time series candidate region extraction network: used to extract candidate time series segments that may have abnormal behavior of substation personnel;
[0087] S4: Build a time-series behavior classification network: perform classification and regression operations on the extracted substation personnel behavior video segments;
[0088] S5: Build an abnormal behavior ...
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