Driving Accident Detection by Self-Supervised Adversarial Appearance-Motion Prediction in First-Person Videos

Driving accident is the event that occur unexpectedly and should be detected effectively for autonomous driving systems. In this paper, we propose a method based on adversarial appearance-motion prediction for driving accident detection in dashcam videos. The novelty of this method is to consider the predictability of the frame-level and object-level motion and appearance from the current to the future. Through a self-supervised adversarial learning between the real observation in next frame and the predicted motion and appearance, the objects that may occur accident are detected. In order to evaluate the accuracy of the detection results of our method, we evaluate the performance on A3D dataset, and the effectiveness of the method is validated.