Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.

Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

Road Traffic Monitoring from UAV Images Using Deep Learning Networks

In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV). The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating the speeds of vehicles on the roads. Our method can automatically measure the speed of the vehicles from the only videos recorded using UAV without additional information in both directions on the roads simultaneously. In our experiments, we evaluate the performance of the proposed method with the visual data at four different locations. The proposed method shows 97.6% recall rate and 94.7% precision rate in detecting vehicles, and it shows error (root mean squared error) of 5.27 km/h in estimating the speeds of vehicles.

EAgLE: Equivalent Acoustic Level Estimator Proposal

Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, Leq, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured Leq and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators.