Lightweight refueling behavior recognition algorithm based on sequence diagrams

Some specific, repetitive actions made by the staffs in the refueling work scenario at the airport can be considered as a way of information transmission, so it is necessary to carry out on-site automatic identification and monitoring of these specific actions to improve the level of supervision. This paper proposes a lightweight refueling behavior recognition algorithm applicable to the field based on video sequences. The algorithm firstly uses the YOLOv3 improved target detection network for human body detection. The resulting human body detection box is tracked using the target tracking algorithm, and the tracked human body sequence maps are input into the behavior classification algorithm based on time-space feature fusion to realize the fast and intelligent analysis of the behavior. The test results of deploying the algorithm to Hi3559A embedded equipment show that the recognition accuracy of the algorithm reached 94.68%, and the inference speed reached 22FPS, which can meet the needs of real-time behavior analysis and processing at the airport refueling site.