论文标题
越过印度道路的数据集和模型
A Dataset and Model for Crossing Indian Roads
论文作者
论文摘要
中型印度城镇中的道路通常有很多交通，但没有（或无视）交通停顿。这使盲人难以安全地越过道路，因为视力对于确定何时安全至关重要。因此，自动且可靠的基于图像的安全分类器有可能帮助盲人穿越印度道路。但是，我们目前缺少从行人观点的印度道路上收集的数据集，并标有道路交叉安全信息。来自其他国家的现有分类器通常用于十字路口，因此依靠交通信号灯的检测和存在，这在印度条件下不适用于。我们介绍了Indra（印度道路交叉数据集），这是第一个数据集，该数据集捕获了来自行人观察点的印度道路视频。 Indra包含104个视频，其中包括26K 1080p帧，每个视频都带有二进制道路交叉安全标签和车辆边界盒。我们培训各种分类器，以预测这些数据的道路交叉安全性，从SVM到卷积神经网络（CNN）。表现最好的模型扩张越野赛是为在Nvidia Jetson Nano上部署而定制的新型单片图像架构。在看不见的图像上，它以90％的精度达到79％的召回。最后，我们提出了一条可穿越的道路交叉助理，跑步了扩张，这可以实时帮助盲人越过印度的道路。项目网页是http://roadcross-assistant.github.io/website/。
Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross Indian roads. Yet, we currently lack datasets collected on Indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in Indian conditions. We introduce INDRA (INdian Dataset for RoAd crossing), the first dataset capturing videos of Indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). The best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross Indian roads in real-time. The project webpage is http://roadcross-assistant.github.io/Website/.
