Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.140
Mykola Maksymiv, Taras Rak
Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.
{"title":"Methods to Increase the Contrast of the Image with Preserving the Visual Quality","authors":"Mykola Maksymiv, Taras Rak","doi":"10.23939/acps2021.02.140","DOIUrl":"https://doi.org/10.23939/acps2021.02.140","url":null,"abstract":"Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123427521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.146
D. Progonov
Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego- to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5% even in the most difficult cases (low cover image payload and absence of cover- stego images pairs in training set).
{"title":"Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods","authors":"D. Progonov","doi":"10.23939/acps2021.02.146","DOIUrl":"https://doi.org/10.23939/acps2021.02.146","url":null,"abstract":"Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego- to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5% even in the most difficult cases (low cover image payload and absence of cover- stego images pairs in training set).","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116702004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.161
Eduard Yakubchykt, I. Yurchak
Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.
{"title":"Image Searching System","authors":"Eduard Yakubchykt, I. Yurchak","doi":"10.23939/acps2021.02.161","DOIUrl":"https://doi.org/10.23939/acps2021.02.161","url":null,"abstract":"Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116057702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.134
Vladyslav Lakhai, Ruslan Bachynskyy
Serverless computing is a new and still evolving type of cloud computing, which brings a new approach to the development of information systems. The main idea of serverless is to give an approach of doing computing without dealing with a server to a user. Such approach allows to reduce the cost of the system building and system support. It allows small companies to concentrate on their own system designing instead of thinking about infrastructure building and supporting. Also, a big problem of providing the system security on high level is on cloud’s provider engineering support service. Serverless approach allows to start business quickly without huge initial investment. There is an attempt to completely analyze features, benefits and drawbacks of serverless approach, its use cases and main patterns of Serverless architecture. What is more, different providers have been analyzed.
{"title":"Investigation of Serverless Architecture","authors":"Vladyslav Lakhai, Ruslan Bachynskyy","doi":"10.23939/acps2021.02.134","DOIUrl":"https://doi.org/10.23939/acps2021.02.134","url":null,"abstract":"Serverless computing is a new and still evolving type of cloud computing, which brings a new approach to the development of information systems. The main idea of serverless is to give an approach of doing computing without dealing with a server to a user. Such approach allows to reduce the cost of the system building and system support. It allows small companies to concentrate on their own system designing instead of thinking about infrastructure building and supporting. Also, a big problem of providing the system security on high level is on cloud’s provider engineering support service. Serverless approach allows to start business quickly without huge initial investment. There is an attempt to completely analyze features, benefits and drawbacks of serverless approach, its use cases and main patterns of Serverless architecture. What is more, different providers have been analyzed.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127646601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.112
B. Havano, M. Morozov
The goal of the work is to propose architectural and information model for assessing the human condition on the basis of microservice architecture in medical cyber-physical system, which, in contrast to the known models for assessing the human condition, can simultaneously provide scaling, fault tolerance and increase the speed of human condition assessment. The theoretical substantiation and the new decision of an actual scientific problem of development and research means of an estimation of a human condition in medical cyber-physical system have been considered. These means involve the parallel processing of data on vital signs of the human condition, organizing the means of information processing into separate independent logical elements — microservices, in comparison with other existing medical cyber-physical systems. An architectural model based on microservice architecture has been proposed.
{"title":"Assessing the Human Condition in Medical Cyber-Physical System Based on Microservices Architecture","authors":"B. Havano, M. Morozov","doi":"10.23939/acps2021.02.112","DOIUrl":"https://doi.org/10.23939/acps2021.02.112","url":null,"abstract":"The goal of the work is to propose architectural and information model for assessing the human condition on the basis of microservice architecture in medical cyber-physical system, which, in contrast to the known models for assessing the human condition, can simultaneously provide scaling, fault tolerance and increase the speed of human condition assessment. The theoretical substantiation and the new decision of an actual scientific problem of development and research means of an estimation of a human condition in medical cyber-physical system have been considered. These means involve the parallel processing of data on vital signs of the human condition, organizing the means of information processing into separate independent logical elements — microservices, in comparison with other existing medical cyber-physical systems. An architectural model based on microservice architecture has been proposed.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122608657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.155
Mykola Voloshyn, Yevhenii Vavruk
The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments. That is why in this work we have developed software for determining the protective elements on the face in real time using the Python scripting language, the open software libraries OpenCV v4.5.4, TensorFlow v2.6.0, Keras v2.6.0 and MobileNetV2 using the camera. The training program uses a prepared set of photos from KAGGLE — with a mask and without a mask. This set has been expanded by the authors to include different types of masks and their location. Using TensorFlow, Keras, MobileNetV2, a model is created to study the neural network by analyzing images. The generated neural network uses a model to determine the masks. You can preview the learning result of the network — it is presented as a graphic file. A program that uses the connected camera is then launched and the user can test the operation. This model can be easily deployed on embedded systems such as Raspberry Pi, Google Coral, and become a hardware and software automated system that can be used in crowded places — airports, shopping malls, stadiums, government agencies and more.
{"title":"Software Implementation of the Algorithm for Recognizing Protective Elements on The Face","authors":"Mykola Voloshyn, Yevhenii Vavruk","doi":"10.23939/acps2021.02.155","DOIUrl":"https://doi.org/10.23939/acps2021.02.155","url":null,"abstract":"The quarantine restrictions introduced during COVID-19 are necessary to minimize the spread of coronavirus disease. These measures include a fixed number of people in the room, social distance, wearing protective equipment. These restrictions are achieved by the work of technological control workers and the police. However, people are not ideal creatures, quite often the human factor makes its adjustments. That is why in this work we have developed software for determining the protective elements on the face in real time using the Python scripting language, the open software libraries OpenCV v4.5.4, TensorFlow v2.6.0, Keras v2.6.0 and MobileNetV2 using the camera. The training program uses a prepared set of photos from KAGGLE — with a mask and without a mask. This set has been expanded by the authors to include different types of masks and their location. Using TensorFlow, Keras, MobileNetV2, a model is created to study the neural network by analyzing images. The generated neural network uses a model to determine the masks. You can preview the learning result of the network — it is presented as a graphic file. A program that uses the connected camera is then launched and the user can test the operation. This model can be easily deployed on embedded systems such as Raspberry Pi, Google Coral, and become a hardware and software automated system that can be used in crowded places — airports, shopping malls, stadiums, government agencies and more.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.098
Maksym Butov, Tetyana Pavych, Y. Paramud
The basic methods and principles of mine safety systems have been considered in the paper. The algorithm of one possible smart device (smart helmet) is depicted. This algorithm describes the basic principles of this device. The device allows to find danger in the environment where the miner works, as well as monitors the condition of the miner. It can also quickly analyze this information and report the danger when it is detected. The system has been developed and programmed including basic modules for implementing this algorithm. The results of the comparative analysis of the new system showed an increase in the level of safety by 45% compared to other systems.
{"title":"Implementation of Smart Technologies in the Mine","authors":"Maksym Butov, Tetyana Pavych, Y. Paramud","doi":"10.23939/acps2021.02.098","DOIUrl":"https://doi.org/10.23939/acps2021.02.098","url":null,"abstract":"The basic methods and principles of mine safety systems have been considered in the paper. The algorithm of one possible smart device (smart helmet) is depicted. This algorithm describes the basic principles of this device. The device allows to find danger in the environment where the miner works, as well as monitors the condition of the miner. It can also quickly analyze this information and report the danger when it is detected. The system has been developed and programmed including basic modules for implementing this algorithm. The results of the comparative analysis of the new system showed an increase in the level of safety by 45% compared to other systems.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123549096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.121
Y. Kohut, I. Yurchak
Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.
{"title":"Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm","authors":"Y. Kohut, I. Yurchak","doi":"10.23939/acps2021.02.121","DOIUrl":"https://doi.org/10.23939/acps2021.02.121","url":null,"abstract":"Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134049444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.090
Dmytro Borovets, Tetyana Pavych, Y. Paramud
Today, there are quite a large number of deaf- mute and hard-of-hearing people which communicate using gestures. Therefore, it is simply necessary to provide them with modern means of communication with the surrounding world. This paper creates a holistic computer system architecture for converting gestures into text and audio messages. The principles of construction and basic design solutions of a computer system based on a modern element base with increased productivity and minimization of hardware costs and energy consumption have been developed. The most popular existing solutions for gesture recognition are considered and analyzed. The operation of the main components has been described, the principle of functioning of the entire system has been analyzed, and their advantages and disadvantages have been compared. The latest structural components for building a computer system (both physical and software) have been selected and investigated. Physical features include: the state-of-the-art Arduino Nano computing platform, the HC-05 Bluetooth module, the ADXL335 accelerometer, and the latest ZD10-100 Information sensor (flexibility sensor). Software features include: firmware for the Arduino Nano hardware platform, Python-based software for splitting the flow of letters into words, displaying them, and voicing them. The methods of Google Media Translation API and Google Text-to-speech (gTTS) have been analyzed. The expediency of conducting research has improved performance through the use of a new information sensor, which is a flexibility sensor ZD10-100 500 g. The general structural scheme of all systems has been designed.
{"title":"Computer System for Converting Gestures to Text and Audio Messages","authors":"Dmytro Borovets, Tetyana Pavych, Y. Paramud","doi":"10.23939/acps2021.02.090","DOIUrl":"https://doi.org/10.23939/acps2021.02.090","url":null,"abstract":"Today, there are quite a large number of deaf- mute and hard-of-hearing people which communicate using gestures. Therefore, it is simply necessary to provide them with modern means of communication with the surrounding world. This paper creates a holistic computer system architecture for converting gestures into text and audio messages. The principles of construction and basic design solutions of a computer system based on a modern element base with increased productivity and minimization of hardware costs and energy consumption have been developed. The most popular existing solutions for gesture recognition are considered and analyzed. The operation of the main components has been described, the principle of functioning of the entire system has been analyzed, and their advantages and disadvantages have been compared. The latest structural components for building a computer system (both physical and software) have been selected and investigated. Physical features include: the state-of-the-art Arduino Nano computing platform, the HC-05 Bluetooth module, the ADXL335 accelerometer, and the latest ZD10-100 Information sensor (flexibility sensor). Software features include: firmware for the Arduino Nano hardware platform, Python-based software for splitting the flow of letters into words, displaying them, and voicing them. The methods of Google Media Translation API and Google Text-to-speech (gTTS) have been analyzed. The expediency of conducting research has improved performance through the use of a new information sensor, which is a flexibility sensor ZD10-100 500 g. The general structural scheme of all systems has been designed.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117019541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.23939/acps2021.02.105
Yevhen Fastiuk, Ruslan Bachynskyy, Nataliia Huzynets
In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.
{"title":"Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework","authors":"Yevhen Fastiuk, Ruslan Bachynskyy, Nataliia Huzynets","doi":"10.23939/acps2021.02.105","DOIUrl":"https://doi.org/10.23939/acps2021.02.105","url":null,"abstract":"In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.","PeriodicalId":188480,"journal":{"name":"Advances in Cyber-Physical Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125021135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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