The Third Eye: Drone, Breakthrough in Inspections and surveillances

Traffic monitoring Drone


Pedestrian and bicycle volume data is one of the most fundamental types of  data for active transportation planning.

 An innovative method that uses drone technology to collect pedestrian and bicycle volume data.

It confirms the feasibility of the technology as an alternative method to collect complex movements of pedestrians and bicycles.

The dataset includes not only the pedestrian and bicycle count data, but also their behavior and characteristics.

The spatiotemporal data can become a valuable resource for a variety of active transportation planning practices and research, including the collection of pedestrian and bicycle volume data in parks and recreational areas, the study of collisions between pedestrians and bicycles, and the analysis of the social path.

The new approaches in embedded vision dedicated to object detection and tracking for drone visual control.

Nevertheless, their deployment in urban environments has so far been limited.

Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way.

In contrast to traditional “map-localize-plan” methods, this letter explores a data-driven approach to cope with the above challenges.

To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city.

Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image:

A steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them.

The challenge is however to collect enough data in an unstructured outdoor environment such as a city.

Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. 

Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians.

Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable.

Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. 

The development and validation of automated vehicles involves a large number of challenges to be overcome.

Due to the high complexity, many classic approaches quickly reach their limits and data-driven methods become necessary.

This creates an unavoidable need for trajectory datasets of road users in all relevant traffic scenarios.

As these trajectories should include naturalistic and diverse behavior, they have to be recorded in public traffic.

Roundabouts are particularly interesting because of the density of interaction between road users, which must be considered by an automated vehicle for behavior planning.

Using a camera-equipped drone, traffic at a total of three different roundabouts in Germany was recorded.

How accurate are small drones for measuring microscopic traffic parameters? 

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.

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.

Top drones for traffic monitoring:

1. NETRA V Series UAV




Switch UAV 



4. DJI Inspire 2

DJI Inspire 2
5. DJI Mavic Air 2

StratoTrans: Unmanned Aerial System (UAS) 4G Communication Framework Applied on the Monitoring of Road Traffic and Linear Infrastructure:

An operational solution to directly connect drones to internet by means of 4G telecommunications and exploit drone acquired data, including telemetry and imagery but focusing on video transmission.

The novelty of this work is the application of 4G connection to link the drone directly to a data server where video (in this case to monitor road traffic) and imagery (in the case of linear infrastructures) are processed.

However, this framework is applicable to any other monitoring purpose where the goal is to send real-time video or imagery to the headquarters where the drone data is processed, analyzed, and utilize.

We describe a general framework and analyze some key points, such as the hardware to use, the data stream, and the network coverage,

but also the complete resulting implementation of the applied unmanned aerial system (UAS) communication system through a Virtual Private Network (VPN) featuring a long-range telemetry high-capacity video link (up to 15 Mbps, 720 p video at 30 fps with 250 ms of latency).

The application results in the real-time exploitation of the video, obtaining key information for traffic managers such as vehicle tracking, vehicle classification, speed estimation, and roundabout in-out matrices.

The imagery downloads and storage is also performed thorough internet, although the Structure from Motion postprocessing is not real-time due to photogrammetric workflows.


Drones can provide on-ground situational awareness in case of emergencies like road accidents, oil leaks etc. and also collect evidence for the same. The data collected by drones can be analyzed to improve traffic flow and road safety.

We describe a real-case application of drone connection to internet thorough 4G network, but it can be adapted to other applications. Although 5G will -in time- surpass 4G capacities, the described framework can enhance drone performance and facilitate paths for upgrading the connection of on-board devices to the 5G network.


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