论文标题
自动驾驶汽车的交通控制手势识别
Traffic Control Gesture Recognition for Autonomous Vehicles
论文作者
论文摘要
汽车司机知道如何对交通官的手势做出反应。显然，除非具有道路交通管制手势识别功能，否则自动驾驶汽车并非如此。在这项工作中，我们解决了现有的自主驾驶数据集的限制，以提供学习数据以识别流量控制。我们介绍了一个基于3D身体骨架输入的数据集，以在每个时间步骤执行流量控制手势分类。我们的数据集由来自几个参与者的250个序列组成，每个序列的范围从16到90秒。为了评估我们的数据集，我们提出了八个基于深层神经网络的顺序处理模型，例如经常性网络，注意机制，时间卷积网络和图形卷积网络。我们对数据集的所有方法以及现实世界的定量评估进行了广泛的评估和分析。代码和数据集可公开使用。
A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. We introduce a dataset that is based on 3D body skeleton input to perform traffic control gesture classification on every time step. Our dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. To evaluate our dataset, we propose eight sequential processing models based on deep neural networks such as recurrent networks, attention mechanism, temporal convolutional networks and graph convolutional networks. We present an extensive evaluation and analysis of all approaches for our dataset, as well as real-world quantitative evaluation. The code and dataset is publicly available.
