论文标题
Driftnet：使用3D Extricnet架构进行积极的驾驶行为分类
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture
论文作者
论文摘要
积极的驾驶（即汽车漂移）是一种危险的行为，它使人类的安全和生命处于重大风险。这种行为被认为是关于公共交通道路正常交通的一种异常。深度学习的最新技术提出了在不同情况下进行异常检测的新方法，例如行人监测，街头战斗和威胁检测。在本文中，我们提出了一种新的异常检测框架，用于检测攻击性驾驶行为。我们的贡献包括基于最先进的EditiveNet 2D图像分类器的3D神经网络体系结构的开发，用于视频中的积极驾驶检测。我们为视频分析提出了一个有效的NET3D CNN功能提取器，并将其与现有功能提取器进行了比较。我们还创建了一个在沙特阿拉伯上下文中的汽车漂移数据集https://www.youtube.com/watch?v=vlzgye1-d1k。据我们所知，这是解决深度学习攻击性驾驶行为问题的第一部作品。
Aggressive driving (i.e., car drifting) is a dangerous behavior that puts human safety and life into a significant risk. This behavior is considered as an anomaly concerning the regular traffic in public transportation roads. Recent techniques in deep learning proposed new approaches for anomaly detection in different contexts such as pedestrian monitoring, street fighting, and threat detection. In this paper, we propose a new anomaly detection framework applied to the detection of aggressive driving behavior. Our contribution consists in the development of a 3D neural network architecture, based on the state-of-the-art EfficientNet 2D image classifier, for the aggressive driving detection in videos. We propose an EfficientNet3D CNN feature extractor for video analysis, and we compare it with existing feature extractors. We also created a dataset of car drifting in Saudi Arabian context https://www.youtube.com/watch?v=vLzgye1-d1k . To the best of our knowledge, this is the first work that addresses the problem of aggressive driving behavior using deep learning.
