The Pedestrian Patterns Dataset
This provides a new dataset for autonomous driving researchers to study pedestrian behavior patterns and risk assessment, though it is incremental as it applies existing detection methods to new data.
The authors introduced a pedestrian patterns dataset for autonomous driving, collected by repeatedly traversing three routes at different times over a week, to capture social and pedestrian behavior patterns for predicting travel risk. They applied Fast R-CNN to count pedestrians in Full HD videos and GPS data, aiming to accelerate research on risk estimation and vision-based localization.
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.