论文标题
公路驾驶数据集用于语义视频细分
Highway Driving Dataset for Semantic Video Segmentation
论文作者
论文摘要
场景理解是语义细分中的必不可少的技术。尽管存在几个可用于语义分割的数据集，但它们主要集中于具有大型深神经网络的语义图像分割。因此，这些网络对于实时应用没有用，尤其是在自动驾驶系统中。为了解决这个问题，我们为语义细分任务做出了两项贡献。第一个贡献是我们介绍了语义视频数据集，即高速公路驱动数据集，这是语义视频分割任务的密集注释的基准。高速公路驱动数据集由20个具有30Hz帧速率的视频序列组成，每个帧都经过密集注释。其次，我们提出了一种利用时间相关性的基线算法。加上我们试图分析时间相关性的尝试，我们期望高速公路驾驶数据集鼓励对语义视频细分进行研究。
Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on semantic image segmentation with large deep neural networks. Therefore, these networks are not useful for real time applications, especially in autonomous driving systems. In order to solve this problem, we make two contributions to semantic segmentation task. The first contribution is that we introduce the semantic video dataset, the Highway Driving dataset, which is a densely annotated benchmark for a semantic video segmentation task. The Highway Driving dataset consists of 20 video sequences having a 30Hz frame rate, and every frame is densely annotated. Secondly, we propose a baseline algorithm that utilizes a temporal correlation. Together with our attempt to analyze the temporal correlation, we expect the Highway Driving dataset to encourage research on semantic video segmentation.
