scholarly journals Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5620
Author(s):  
Donghui Shan ◽  
Tian Lei ◽  
Xiaohong Yin ◽  
Qin Luo ◽  
Lei Gong
Keyword(s):  
Field Experiment ◽  
Systematic Approach ◽  
Traffic Monitoring ◽  
Operating Speed ◽  
Data Validation ◽  
Vehicle Data ◽  
Strong Robustness ◽  
Model Training ◽  
Sort Algorithm ◽  
Easy Operation

The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km/h to 90 km/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within ±3 km/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%.

First Steps in Beginning a Meta-analysis

2013 ◽  
Author(s):  
Gavin B. Stewart ◽  
Isabelle M. Côté ◽  
Hannah R. Rothstein ◽  
Peter S. Curtis
Keyword(s):  
Field Experiment ◽  
Systematic Approach ◽  
Sampling Error ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Systematic Research ◽  
Different Types ◽  
Statistical Approaches ◽  
Definition Of ◽  
Meta Analyses

This chapter discusses the initiation of the process of systematic research synthesis. Without a systematic approach to defining, obtaining, and collating data, meta-analyses may yield precise but erroneous results, with different types of sampling error (biases) and excess subjectivity in choice of methods and definition of thresholds; these devalue the rigor of any statistical approaches employed. The chapter considers exactly the same issues that face an ecologist designing a field experiment. What's the question? How can I define my sampling universe? How should I collect my data? What analyses should I undertake? How should I interpret my results robustly? These questions are considered in the context of research synthesis.

scholarly journals Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform

2020 ◽  
Vol 10 (17) ◽  
pp. 5763
Author(s):  
Sebastian Cygert ◽  
Andrzej Czyżewski
Keyword(s):  
Video Processing ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Fine Tuning ◽  
Estimation Accuracy ◽  
Real Time Processing ◽  
Embedded Platform ◽  
Human Effort ◽  
The Subject ◽  
Sort Algorithm

Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper.

scholarly journals Modelling the Operating Speed in Segments of Two-Lane Highways from Probe Vehicle Data: A Stochastic Frontier Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
António Lobo ◽  
Marco Amorim ◽  
Carlos Rodrigues ◽  
António Couto
Keyword(s):  
Stochastic Frontier ◽  
Operating Speed ◽  
Design Elements ◽  
New Model ◽  
Horizontal Curves ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Road Planning ◽  
Design Consistency ◽  
Definition Of

Most of the existing operating speed statistical models are applicable to individual design elements, particularly horizontal curves and tangents. A segment approach to operating speed has rarely been followed, with a few exceptions mainly related to the performance assessment of urban and freeway corridors, or design consistency studies using speed profiles built from successive design elements. This study introduces a new model to predict operating speeds in segments of two-lane highways. The maximum operating speed is given by a stochastic frontier function of the average daily traffic and road geometrics; the asymmetric disturbance accounts for the diversity in drivers’ behaviour and vehicle characteristics, allowing estimating any percentile speed. The model was calibrated using probe vehicle data from noncongested roads. The accuracy of the average daily traffic in representing the actual driving conditions was further validated using simultaneous speed-traffic measurements. The new model aims to assist practitioners in the evaluation of design consistency from a macroscopic perspective since the early stages of road planning and design, as well as to support the definition of speed limits at new or existing infrastructures.

scholarly journals The Process of Designing Artificial Intelligence: Development Trends and Prospects

2021 ◽  
Vol 8 (3) ◽  
pp. 84-92
Author(s):  
Eugeniu B. Cozac
Keyword(s):  
Artificial Intelligence ◽  
Systematic Approach ◽  
Rapid Development ◽  
Human Intelligence ◽  
Economic Activities ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Digital World ◽  
Model Training ◽  
Intelligence Development

The relevance of research is due to the rapid development of artificial intelligence. It is an important technology that supports everyday social, technical, and economic activities. Artificial intelligence allows computers to learn from their own experience, adapt to set parameters, and perform tasks that were previously only possible for humans. In this regard, this article is aimed at identifying trends and prospects for the development of artificial intelligence. Another considerable task is to highlight the principles of building artificial intelligence systems. Developing an artificial intelligence system differs from building a conventional system as it requires a systematic approach, big data analysis, and model training. Building an artificial intelligence system − is a detailed process of reverse engineering human traits, capabilities of a machine, and using its computational power to surpass humans' skills. The leading approach to the study of this issue is literature analysis, which makes it possible to comprehensively consider artificial intelligence development. This article includes the modern foundations of artificial intelligence and various representative applications. In the context of the modern digital world, artificial intelligence is the property of machines, computer programmes and systems to perform intellectual and creative human functions, independently find ways to solve issues, be able to draw conclusions and make decisions. The research materials are of practical value for a critical analysis of current artificial intelligence capabilities, reasons why it still cannot achieve human intelligence, and the challenges it faces when achieving and surpassing the level of human intelligence

Reading Longer Words: Insights Into Multisyllabic Word Reading

2017 ◽  
Vol 2 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Lindsay Heggie ◽  
Lesly Wade-Woolley
Keyword(s):  
Systematic Approach ◽  
Word Reading ◽  
Explicit Instruction ◽  
Working Memory Capacity ◽  
Reading Difficulties ◽  
Instructional Programs ◽  
Morphological Knowledge ◽  
Morphological Complexity ◽  
Reading Accuracy ◽  
Grade 3

Students with persistent reading difficulties are often especially challenged by multisyllabic words; they tend to have neither a systematic approach for reading these words nor the confidence to persevere (Archer, Gleason, & Vachon, 2003; Carlisle & Katz, 2006; Moats, 1998). This challenge is magnified by the fact that the vast majority of English words are multisyllabic and constitute an increasingly large proportion of the words in elementary school texts beginning as early as grade 3 (Hiebert, Martin, & Menon, 2005; Kerns et al., 2016). Multisyllabic words are more difficult to read simply because they are long, posing challenges for working memory capacity. In addition, syllable boundaries, word stress, vowel pronunciation ambiguities, less predictable grapheme-phoneme correspondences, and morphological complexity all contribute to long words' difficulty. Research suggests that explicit instruction in both syllabification and morphological knowledge improve poor readers' multisyllabic word reading accuracy; several examples of instructional programs involving one or both of these elements are provided.

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