End-to-end Parking Behavior Recognition Based on Self-attention Mechanism

Abstract

In response to the current problem of a large amount of abnormal data in parking behavior detection, this research proposes a network specialized in parking behavior identification, which identifies the background parking behavior data, classifies the data with high accuracy, reduces the cost of manually verifying the data in the background, speeds up the parking charging cycle of enterprises, and optimizes the user experience.The dynamic position embedding is introduced in the parking-transformer species, so that the self-attention within the transformer can dynamically model the structure of the input token and dynamically encode the input parking behavior sequence data to improve the accuracy of the model for parking behavior recognition.In addition, we created a self-collected parking behavior(SPB) dataset, which was acquired in a natural state and contained various behaviors, and manually classified the various behaviors within the data, and then randomly divided into a test set and a validation set for training and testing, respectively.Compared with the existing methods, indicate that parking-trasnformer hits acceptable trade-offs,namely,97.14% accuracy for SPB dataset.