EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data
EgoSpeed-net: forecasting speed-control in driver behavior from egocentric video data
复制标题
DOI：
10.1145/3557915.3560946
复制
发表时间：
2022-09
期刊：
影响因子：
--
通讯作者：
Yichen Ding;Ziming Zhang;Yanhua Li;Xun Zhou
中科院分区：
文献类型：
--
作者：
Yichen Ding;Ziming Zhang;Yanhua Li;Xun Zhou
速度控制预测是驾驶员行为分析中一个具有挑战性的问题，旨在预测驾驶员在控制车速（例如制动或加速度）中的未来行动。在本文中，我们试图仅使用以自我为中心的视频数据来应对这一挑战，与文献中的大多数作品使用第三人称视图数据或额外的车辆传感器数据（例如GPS或两者）相比，我们提出了一个新颖的图形卷积网络（GCN）网络，即基于Egospeed-Net。我们的动机是，随着时间的流逝，对象的位置变化可以为我们提供非常有用的线索，以预测未来的速度变化。我们首先使用完全连接的图形在每个类别的对象之间对物体之间的空间关系进行建模，并在其上将GCN应用于特征提取。然后，我们利用一个长期的短期内存网络随着时间的推移将这些功能融合到矢量中，加入此类矢量并使用多层perceptron分类器预测速度控制动作。我们在本田研究所驾驶数据集上进行了广泛的实验，并证明了Egospeed-net的卓越性能。
Speed-control forecasting, a challenging problem in driver behavior analysis, aims to predict the future actions of a driver in controlling vehicle speed such as braking or acceleration. In this paper, we try to address this challenge solely using egocentric video data, in contrast to the majority of works in the literature using either third-person view data or extra vehicle sensor data such as GPS, or both.To this end, we propose a novel graph convolutional network (GCN) based network, namely, EgoSpeed-Net. We are motivated by the fact that the position changes of objects over time can provide us very useful clues for forecasting the speed change in future. We first model the spatial relations among the objects from each class, frame by frame, using fully-connected graphs, on top of which GCNs are applied for feature extraction. Then we utilize a long short-term memory network to fuse such features per class over time into a vector, concatenate such vectors and forecast a speed-control action using a multilayer perceptron classifier. We conduct extensive experiments on the Honda Research Institute Driving Dataset, and demonstrate superior performance of EgoSpeed-Net.