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Small Unmanned Aerial Vehicles (sUAV or drones) have been one of the latest tools for monitoring transportation infrastructure and operations. Their lower cost compared to current fixed location camera systems or Manned Aerial Vehicles (MAV) and their ability to read just their view area depending on the situation they face, make them a promising tool of collecting both macroscopic and microscopic data. However, although drone technology and computer vision techniques are advancing fast, there is little information on how accurate and reliable they are for collecting microscopic traffic data. In this paper, we examine the potential of using sUAV as part of the ITS infrastructure as a way of extracting naturalistic trajectory data from aerial video footage from a low volume four-way intersection and a pedestrian passage. Moreover, the accuracy of speed data collected from a drone compared to data collected from an On-Board Diagnostics II (OBD-II) device is examined. For this, a controlled experiment where the vehicle was driven in various speeds and the drone flew in ranging altitudes was conducted. Results show that accuracy is highly dependent on the stabilization of the video and the geo-reference procedure. Moreover, the capabilities of such systems are examined in traffic applications and the way they can be part of future transportation infrastructure is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios. [ABSTRACT FROM AUTHOR]