论文标题
行人模式数据集
The Pedestrian Patterns Dataset
论文作者
论文摘要
我们介绍用于自动驾驶的行人模式数据集。从不同的特定时间表开始，通过反复穿越相同的三个路线来收集数据集。数据集的目的是在不同时间捕获沿着经过的路线的社会和行人行为的模式，并最终使用此信息来预测与沿着不同路线自主行进相关的风险。该数据集包含每个遍历的完整高清视频和GPS数据。将快速的R-CNN行人检测方法应用于捕获的视频，以计算每个视频框架上行人的数量，以评估沿路线的行人密度。通过向研究人员提供此大规模数据集，我们希望加速自动驾驶研究，不仅要估算公众和自动驾驶汽车的风险，而且还加速了对基于长期视力的移动机器人和未来自动驾驶汽车的长期研究。
We present the pedestrian patterns dataset for autonomous driving. The dataset was collected by repeatedly traversing the same three routes for one week starting at different specific timeslots. The purpose of the dataset is to capture the patterns of social and pedestrian behavior along the traversed routes at different times and to eventually use this information to make predictions about the risk associated with autonomously traveling along different routes. This dataset contains the Full HD videos and GPS data for each traversal. Fast R-CNN pedestrian detection method is applied to the captured videos to count the number of pedestrians at each video frame in order to assess the density of pedestrians along a route. By providing this large-scale dataset to researchers, we hope to accelerate autonomous driving research not only to estimate the risk, both to the public and to the autonomous vehicle but also accelerate research on long-term vision-based localization of mobile robots and autonomous vehicles of the future.
