BrnoCompSpeed Dataset

University Transportation Centers Program2022ZIPDatasetMobility21, Carnegie Mellon UniversityUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyMobility21, Carnegie Mellon UniversityUnited StatesForecastingPedestrian movementTraffic safetyTraffic surveillanceVehicle trajectoriesVideo69A3551747111In this project, the authors have developed components of a system to identify and measure vehicle and pedestrian trajectories, speed, and inter-vehicle gaps using video feeds from arbitrary traffic surveillance cameras. The potential impact to transportation safety is the ability to detect crashes in real-time and capture near crashes or accidents and their context. Real-time analysis allows for immediate notification, detecting traffic density and speeds. Alerting safety planners to near-miss crash events and related contextual information, could provide critical information for safety enhancement through appropriate infrastructure safety modifications. The authors also generalized this research to deal with large scale analysis of vehicle activities, and tracking of multiple cars with the ability to re-identify the same cars at different city roads from different cameras. In parallel, the authors also decided to focus on the prediction pedestrian routes and trajectories in a city as well as in typical road situations.131