Abnormal Driving Behavior Detection Using ST-GCN

Abstract

The purpose of this study is to automatically detect abnormal driving behavior from in-vehicle camera video. The previous method used a Multi-stream CNN based on the original image and optical flow. However, this dataset could not outperform the accuracy rate of a CNN using the original image as input. Therefore, we propose a method to improve the accuracy of Abnormal driving behavior detection by using ST-GCN with skeleton as input and combining it with CNN using the original image as input. Furthermore, we prepared two input coordinate systems (Cartesian and polar coordinates) and four data augmentations (affine transformation, left-right flipping, dropout for joints, and adding Gaussian noise) for ST-GCN. we investigated combinations of ST-GCN input coordinate systems and data augmentations that are effective for this task.