scholarly journals A Novel Approach for Automatic Car Plate Detection and Recognition

2021 ◽  
Vol 5 (2) ◽  
pp. 1-9
Author(s):  
Fattah Alizadeh ◽  
Sazan Luqman
Keyword(s):  
Traffic Control ◽  
Automatic System ◽  
High Performance ◽  
Recognition System ◽  
Input Image ◽  
Novel Approach ◽  
Computer Based ◽  
The City ◽  
Processing Steps ◽  
Do So

The increasing number of cars inside cities creates problems in traffic control. This issue can be solved by implementing a computer-based automatic system known as the Automatic Car Plate Recognition System (ACPRS). The main purpose of the current paper is to propose an automatic system to detect, extract, segment, and recognize the car plate numbers in the Kurdistan Region of Iraq (KRI). To do so, a frontal image of cars is captured and used as an input of the system. After applying the required pre-processing steps, the SURF descriptor is utilized to detect and extract the car plate from the whole input image. After segmentation of the extracted plate, an efficient projection-based technique is being exploited to describe the available digits and the city name of the registered car plate. The system is evaluated over 200 sample images, which are taken under various testing conditions. The best accuracy of the proposed system, under the controlled condition, shows the high performance and accuracy of the system which is 94%.

scholarly journals Muskmelon Maturity Stage Classification Model Based on CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huamin Zhao ◽  
Defang Xu ◽  
Olarewaju Lawal ◽  
Shujuan Zhang
Keyword(s):  
Data Augmentation ◽  
Input Image ◽  
Classification Model ◽  
Maturity Stage ◽  
Complex Environments ◽  
Excellent Performance ◽  
Novel Approach ◽  
Stage Classification ◽  
Computer Based ◽  
Indoor And Outdoor

How to quickly and accurately judge the maturity of muskmelon is very important to consumers and muskmelon sorting staff. This paper presents a novel approach to solve the difficulty of muskmelon maturity stage classification in greenhouse and other complex environments. The color characteristics of muskmelon were used as the main feature of maturity discrimination. A modified 29-layer ResNet was applied with the proposed two-way data augmentation methods for the maturity stages of muskmelon classification using indoor and outdoor datasets to create a robust classification model that can generalize better. The results showed that code data augmentation which is the first way caused more performance degradation than input image augmentation—the second way. This established the effectiveness of the code data augmentation compared to image augmentation. Nevertheless, the two-way data augmentations including the combination of outdoor and indoor datasets to create a classification model revealed an excellent performance of F1 score ∼99%, and hence the model is applicable to computer-based platform for quick muskmelon stages of maturity classification.

scholarly journals Sub-Pixel Chessboard Corner Localization for Camera Calibration and Pose Estimation

2018 ◽  
Vol 8 (11) ◽  
pp. 2118 ◽  
Author(s):  
Tianlong Yang ◽  
Qiancheng Zhao ◽  
Xian Wang ◽  
Quan Zhou
Keyword(s):  
Camera Calibration ◽  
Pose Estimation ◽  
High Performance ◽  
Vision System ◽  
Low Cost ◽  
Input Image ◽  
Point Problem ◽  
Novel Approach ◽  
Site Use ◽  
Common Vision

This work describes a novel approach to localize sub-pixel chessboard corners for camera calibration and pose estimation. An ideally continuous chessboard corner model is established, as a function of corner coordinates, rotation and shear angles, gain and offset of grayscale, and blurring strength. The ideal model is evaluated by a low-cost and high-similarity approximation for sub-pixel localization, and by performing a nonlinear fit to input image. A self-checking technique is also proposed by investigating qualities of the model fits, for ensuring the reliability of addressing perspective-n-point problem. The proposed method is verified by experiments, and results show that it can share a high performance. It is also implemented and examined in a common vision system, which demonstrates that it is suitable for on-site use.

scholarly journals A Review Paper on Automatic Number Plate Recognition System

2020 ◽  
pp. 955-966
Author(s):  
Shally Gupta ◽  
Rajesh Shyam Singh ◽  
H. L. Mandoria
Keyword(s):  
Image Processing ◽  
Traffic Control ◽  
Character Recognition ◽  
Large Scale ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Transportation System ◽  
Input Image ◽  
Image Processing Technique ◽  
License Plate

Number plate recognition brings a drastic improvement for the city traffic enhancement. It provides the direction in which the steps should be taken for working of an effective intelligent transportation system. ANPR have become necessity for traffic control management due to rapid increment of vehicles. The main aim of ANPR is to monitor traffic and for security purpose. Recognition of number plate uses image processing techniques and latest technology in detecting characters on vehicle license plates automatically. In recent years, there are many technological developments in recognizing license plate area of research. Image processing protocols like OCR technology allow the traffic surveillance to deal with several problems that occurs in criminal investigation, toll collection, monitoring traffic, controlling speed, parking management etc. For efficient management of traffic and mass surveillance in transportation system, an ANPR system is essential. With the aid of image processing algorithms and vehicle images dataset, it becomes possible to monitor traffic at a large scale. Vehicle images are helpful for recognizing characters on license plates by performing image segmentation, feature extraction and character recognition. Data collected through captured images are utilized in the commercial applications, law enforcement, traffic applications etc. The software examines the vehicle picture as an input image that results in displaying the plate numbers. The system with image processing used reliably for traffic detection where modification of the technologies enables an accurate acknowledgement of vehicle number plates. There are several ANPR systems developed, working on character recognition of LP by the help of image processing technique. This paper reviews the performance by researchers in this particular area towards meeting goals of transportation system. It also provides major issues and challenges in this field.

scholarly journals ESTIMATION OF THE LOGIT MODEL FOR THE ONLINE CONTRAFLOW PROBLEM

Transport ◽  
2010 ◽  
Vol 25 (4) ◽  
pp. 433-441
Author(s):  
Habibollah Nassiri ◽  
Ali Edrissi ◽  
Hamed Alibabai
Keyword(s):  
Homeland Security ◽  
Logit Model ◽  
High Performance ◽  
Choice Model ◽  
Calibration Data ◽  
Fort Worth ◽  
Data Set ◽  
Novel Approach ◽  
The City ◽  
Potential Remedy

Contraflow or lane reversal is an efficient way for increasing the outbound capacity of a network by reversing the direction of in‐bound roads during evacuations. Hence, it can be considered as a potential remedy for solving congestion problems during evacuation in the context of homeland security, natural disasters and urban evacuations, especially in response to an expected disaster. Most of the contraflow studies are performed offline, thus strategies are generated beforehand for future implementation. Online contraflow models, however, would be often computationally demanding and time‐consuming. This study contributes to the state of the art of contraflow modelling in two regards. First, it focuses on the calibration of a Logit choice model which predicts the online contraflow directions of strategic lanes based on the set of directions obtained from offline scenarios. This is the first effort to adjust offline results to be applied for an online case. The second contribution of this paper is the generation of calibration data set from a novel approach through simulation. The calibrated Logit model is then tested for the network of the City of Fort Worth, Texas. The results show a high performance of this approach to generating beneficial strategies, including an increase in up to 16% in throughput compared to no contraflow case.

Development of the automated information system «clean district» for control of cleaning of streets in the city of Krasnoyarsk

2020 ◽  
pp. 77-83
Author(s):  
A. Popov ◽  
O.N. Lopateeva ◽  
A.K. Ovsyankin ◽  
M. M. Satsuk ◽  
A. A. Artyshko ◽  
...  
Keyword(s):  
Information System ◽  
Quality Control ◽  
High Performance ◽  
Operational Performance ◽  
Development Environment ◽  
Integrated Development ◽  
Central District ◽  
Effective Performance ◽  
Automated Information System ◽  
The City

Among the measures aimed at the effective performance of public services in a modern urban environment, one of the main is the quality control and efficiency of the work performed. Timely street cleaning is hampered by several groups of problems, including the lack of a single automated information system (AIS) control of the work performed. In this regard, there is a need to improve and automate this area. This approach will allow you to combine high performance due to the speed of the system and effective quality control of street cleaning. The purpose of this work is the study and analysis of existing information systems (is), allowing to automate the process of quality control and operational performance of the above tasks. On the basis of the conducted researches, to develop is, having coordinated with the customer (administration of the Central district of Krasnoyarsk) requirements and functionality which allow to automate this process.This article presents the main aspects of the design and software solutions for the implementation of the algorithm in the form of AIS, designed to automate the process of monitoring the cleanliness of streets in the city. The development of AIS was conducted in the PhpStorm integrated development environment in the PHP programming language.

Design and Group Creativity

2019 ◽  
pp. 305-318
Author(s):  
Arthur B. Markman ◽  
Jonathan Cagan
Keyword(s):  
Practical Reason ◽  
Group Processes ◽  
Theoretical Studies ◽  
Design Development ◽  
Group Creativity ◽  
Mental Processes ◽  
Creative Solutions ◽  
End Products ◽  
Computer Based ◽  
Do So

Design communities in engineering and other disciplines have a practical reason for caring about group creativity. People employed in these areas have to generate creative solutions routinely, and they often must do so in a group. As a result, research in these areas has focused on processes to improve group creativity. This chapter explores techniques for generating problem statements and solutions in groups that have emerged from this literature. It also examines computer-based methods of problem solving that groups can use to enhance the ideas that arise from these group processes. This work has expanded the range of elements explored in studies of group creativity. Although theoretical studies of creativity can be useful in uncovering underlying mental processes, design development requires useful end products. The focus of this research on techniques that enhance creativity in design provides an opportunity to link this literature with the broader literature on individual and group creativity.

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

scholarly journals Human emotion modeling (HEM): an interface for IoT systems

2021 ◽  
Author(s):  
Mohammed R. Elkobaisi ◽  
Fadi Al Machot
Keyword(s):  
Assisted Living ◽  
High Performance ◽  
Modeling Language ◽  
Emotional States ◽  
Human Emotion ◽  
Care Givers ◽  
Novel Approach ◽  
Domain Specific Modeling ◽  
Increasing Demand ◽  
Human Support

AbstractThe use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capable to provide such a combination. In this sense, we aim at providing a novel approach based on a modeling language that can be used even by care-givers or non-experts to model human emotion w.r.t. context for human support services. The proposed modeling approach is based on Domain-Specific Modeling Language (DSML) which helps to integrate different IoT data sources in AAL environment. Consequently, it provides a conceptual support level related to the current emotional states of the observed subject. For the evaluation, we show the evaluation of the well-validated System Usability Score (SUS) to prove that the proposed modeling language achieves high performance in terms of usability and learn-ability metrics. Furthermore, we evaluate the performance at runtime of the model instantiation by measuring the execution time using well-known IoT services.

scholarly journals Effects of COVID-19 on Urban Population Flow in China

2021 ◽  
Vol 18 (4) ◽  
pp. 1617 ◽  
Author(s):  
Xiaorong Jiang ◽  
Wei Wei ◽  
Shenglan Wang ◽  
Tao Zhang ◽  
Chengpeng Lu
Keyword(s):  
Network Topology ◽  
Traffic Control ◽  
Urban Population ◽  
Deep Understanding ◽  
Clustering Coefficient ◽  
Spatial Distance ◽  
Structure Evolution ◽  
Effective Distance ◽  
Urban Hierarchy ◽  
The City

The COVID-19 epidemic has become a Public Health Emergency of International Concern. Thus, this sudden health incident has brought great risk and pressure to the city with dense population flow. A deep understanding of the migration characteristics and laws of the urban population in China will play a very positive role in the prevention and control of the epidemic situation. Based on Baidu location-based service (LBS) big data, using complex networks method and geographic visualization tools, this paper explores the spatial structure evolution of population flow network (PFN) in 368 cities of China under different traffic control situations. Effective distance models and linear regression models were established to analyze how the population flow across cities affects the spread of the epidemic. Our findings show that: (1) the scope of population flow is closely related to the administrative level of the city and the traffic control policies in various cities which adjust with the epidemic situation; The PFN mainly presents the hierarchical structure dominated by the urban hierarchy and the regional isolation structure adjacent to the geographical location.(2) through the analysis network topology structure of PFN, it is found that only the first stage has a large clustering coefficient and a relatively short average path length, which conforms to the characteristics of small world network. The epidemic situation has a great impact on the network topology in other stages, and the network structure tends to be centralized. (3) The overall migration scale of the whole country decreased by 36.85% compared with the same period of last year’s lunar calendar, and a further reduction of 78.52% in the nationwide traffic control stage after the festival. (4) Finally, based on the comparison of the effective distance and the spatial distance from the Wuhan to other destination cities, it is demonstrated that there is a higher correlation between the effective distance and the epidemic spread both in Hubei province and the whole country.

scholarly journals Tracking by Deblatting

2021 ◽  
Author(s):  
Denys Rozumnyi ◽  
Jan Kotera ◽  
Filip Šroubek ◽  
Jiří Matas
Keyword(s):  
High Speed ◽  
High Performance ◽  
Motion Blur ◽  
Velocity Estimation ◽  
High Speed Camera ◽  
Trajectory Function ◽  
Novel Approach ◽  
Blind Deblurring ◽  
Object Trajectories ◽  
Complex Trajectories

AbstractObjects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects travel a considerable distance during exposure time of a single frame, and therefore, their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by general trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. By postprocessing, non-causal Tracking by Deblatting estimates continuous, complete, and accurate object trajectories for the whole sequence. Tracked objects are precisely localized with higher temporal resolution than by conventional trackers. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are then fitted to smooth trajectory segments between bounces. The output is a continuous trajectory function that assigns location for every real-valued time stamp from zero to the number of frames. The proposed algorithm was evaluated on a newly created dataset of videos from a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms the baselines both in recall and trajectory accuracy. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity, and sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall, and velocity estimation.

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