At present

the field safety monitoring relies on the professionals to monitor the surveillance video throughout the process

and the manual detection method is very timeconsuming and prone to false alarms

which is difficult to ensure the personal security during the operation. In order to realize the intelligent recognition of human behaviors in power operation field

an OpenPosebased method for detecting dangerous behaviors of electric power workers is proposed. This method extracts the key point information of electric workers′ skeletons from the video stream images

and uses deep neural network to realize human behavior situation awareness in multiperson scenarios

which can realize the realtime detection of construction personnels′ illegal behavior and issue early warning. The proposed method realizes the accurate and realtime safety monitoring of human behaviors in power operation field

and guarantees the personal security in the field and the smooth progress of power operation

and the model has a good robustness and generalization ability.