For Safer Navigation: Pedestrian-View Intersection Classification
For Safer Navigation: Pedestrian-View Intersection Classification
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为了更安全的导航：行人视野交叉口分类
DOI：
10.1109/ictc49870.2020.9289182
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Seung
中科院分区：
文献类型：
--
作者：
M. Astrid;Jin;Muhammad Zaigham Zaheer;Jae;Seung
交叉口分类是自主导航的关键组成部分之一。作为先行任务，开展了多项相关研究工作，解决自动驾驶、飞机悬停、矿井导航等问题。然而，据我们所知，这些研究均不支持行人视觉导航来引导小型且缓慢的机器人，因为在普通道路上与普通车辆一起操作太危险。为了解决这个问题，我们提出：1）行人视图级交叉口分类图像数据集，2）在所提出的数据集上微调的基于 ResNet 的架构，以及 3）彻底的实验来探索我们所提出的架构的功能。本文报告的详细分析使我们能够找到既不欠拟合也不过拟合我们的数据的网络配置，并实现 80% 的测试精度。
Intersection classification is one of the key components of autonomous navigation. Several related research works have been conducted as a prior task to solve problems such as autonomous driving, aircraft hovering, and navigating in mines. However, to the best of our knowledge, none of these studies support pedestrian-view navigation to guide the small and slow robots for which it is too dangerous to be operated on normal roads along with normal vehicles. To address this problem, we propose: 1) a pedestrian-view-level intersection classification image dataset, 2) ResNet-based architecture fine-tuned on the proposed dataset, and 3) thorough experimentation to explore the capabilities of our proposed architecture. The detailed analysis reported in this paper enabled us to find the network configuration that is neither underfit nor overfit to our data and achieves 80% test accuracy.