Pub Date : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988184
Giridharan N, S. R
Forest is backbone of Earth's life. Recently, Remote Sensing (RS) and Geographic information system (GIS) techniques have detailed information on forest cover changes. The present work envisions that the changes in forest cover are investigated by the high-resolution satellite data (HRSD) with the help of Normalized Difference Vegetation Index (NDVI) based image processing technique in Sathyamangalam Forest, Erode District. The Multi-Temporal imagery-based six individual NDVI maps (2016 to 2021) were fixed using ArcGIS software. The importance of NDVI was performed to notice the changes in the forest cover region. The comprehensive study shows that the changes in forest cover deliberate from minimum to maximum immortal area with 197.17 sq. km (2016) and 364.19 sq. km (2021), respectively. Finally, this result predicts that sustainable growth needs to monitor for further development in the future.
{"title":"NDVI based Image Processing for Forest change Detection in Sathyamangalam Reserve Forest","authors":"Giridharan N, S. R","doi":"10.1109/ICTACS56270.2022.9988184","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988184","url":null,"abstract":"Forest is backbone of Earth's life. Recently, Remote Sensing (RS) and Geographic information system (GIS) techniques have detailed information on forest cover changes. The present work envisions that the changes in forest cover are investigated by the high-resolution satellite data (HRSD) with the help of Normalized Difference Vegetation Index (NDVI) based image processing technique in Sathyamangalam Forest, Erode District. The Multi-Temporal imagery-based six individual NDVI maps (2016 to 2021) were fixed using ArcGIS software. The importance of NDVI was performed to notice the changes in the forest cover region. The comprehensive study shows that the changes in forest cover deliberate from minimum to maximum immortal area with 197.17 sq. km (2016) and 364.19 sq. km (2021), respectively. Finally, this result predicts that sustainable growth needs to monitor for further development in the future.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780253","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987782
Priscilla Whitin, V. Jayasankar
The Covid-19 pandemic created a massive impact on various sectors across the globe. Nearly 400 million people have been affected by Covid-19 as of January 2022. Although vaccines have been developed, only 49.8% of world population have been vaccinated. The W.H.O has advised the public to maintain social distance in crowded places and wear well fitted mask to impede the spread of corona virus. It has been made mandatory by most countries to wear mask in public places, human monitoring continuously is impossible hence we deploy Deep learning model to implement the same. In this paper we have trained mobilenetV2 architecture for facemask detection using custom dataset. The accuracy of the model in real time is 99.99%
{"title":"Deep Learning Based Facemask Detection","authors":"Priscilla Whitin, V. Jayasankar","doi":"10.1109/ICTACS56270.2022.9987782","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987782","url":null,"abstract":"The Covid-19 pandemic created a massive impact on various sectors across the globe. Nearly 400 million people have been affected by Covid-19 as of January 2022. Although vaccines have been developed, only 49.8% of world population have been vaccinated. The W.H.O has advised the public to maintain social distance in crowded places and wear well fitted mask to impede the spread of corona virus. It has been made mandatory by most countries to wear mask in public places, human monitoring continuously is impossible hence we deploy Deep learning model to implement the same. In this paper we have trained mobilenetV2 architecture for facemask detection using custom dataset. The accuracy of the model in real time is 99.99%","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132784582","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987844
M. Reddy, R. Sumathi, N. K. Reddy, N. Revanth, S. Bhavani
Prediction of prices in the Stock Market is a complex task. It involves more contact between humans and computers. We will use more efficient algorithms to get the result more accurate. The proposed methodology here is Linear Regression, Ridge Regression, Lasso Regression and Polynomial Regression. This case will provide us the accurate results and this experiment results are effective and suitable for prediction. Firstly we will collect the data from the kaggle, then we will apply the proposed algorithms and the code is changed according to the results we get the accuracy we are getting. Finally this includes the workflow of the prediction of the share market. The results from the experiment can show that the methodology suggested is remarkably productive and also appropriate for predicting before a short period of time.
{"title":"Analysis of Various Regressions for Stock Data Prediction","authors":"M. Reddy, R. Sumathi, N. K. Reddy, N. Revanth, S. Bhavani","doi":"10.1109/ICTACS56270.2022.9987844","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987844","url":null,"abstract":"Prediction of prices in the Stock Market is a complex task. It involves more contact between humans and computers. We will use more efficient algorithms to get the result more accurate. The proposed methodology here is Linear Regression, Ridge Regression, Lasso Regression and Polynomial Regression. This case will provide us the accurate results and this experiment results are effective and suitable for prediction. Firstly we will collect the data from the kaggle, then we will apply the proposed algorithms and the code is changed according to the results we get the accuracy we are getting. Finally this includes the workflow of the prediction of the share market. The results from the experiment can show that the methodology suggested is remarkably productive and also appropriate for predicting before a short period of time.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132832203","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988465
K. S, T. Vyshnavi, Yaragandla Mounika, S. Tejaswini
Within this paper, we point to consider the plausibility of recognizing spams in mobile phone sms messages by recommending an improved Converter method. This method is planned for recognizing spams in SMS messages. We use “Spam Collection v.1 dataset” as well as “UtkMl's Twitter Spam Location Competition” dataset to evaluate our proposed spam Detector, with a number of well-known machine learning classifiers and cutting-edge SMS spam detection techniques serving as the benchmarks. In our paper, we use networks such by way of long short term memory (LSTM), bi-directional LSTM, and encoder-decoder LSTM models which are recurrent neural networks. Our investigations on SMS spam detection demonstrate that the proposed improved spam Converter outperforms all other alternatives regarding accuracy, F1-Score and recall. Additionally, the suggested model performs well on UtkMl's Twitter dataset, suggesting a favorable chance of applying model to other similar issues.
{"title":"A Revised Converter Paradigm Designed for Spam Message Exposure","authors":"K. S, T. Vyshnavi, Yaragandla Mounika, S. Tejaswini","doi":"10.1109/ICTACS56270.2022.9988465","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988465","url":null,"abstract":"Within this paper, we point to consider the plausibility of recognizing spams in mobile phone sms messages by recommending an improved Converter method. This method is planned for recognizing spams in SMS messages. We use “Spam Collection v.1 dataset” as well as “UtkMl's Twitter Spam Location Competition” dataset to evaluate our proposed spam Detector, with a number of well-known machine learning classifiers and cutting-edge SMS spam detection techniques serving as the benchmarks. In our paper, we use networks such by way of long short term memory (LSTM), bi-directional LSTM, and encoder-decoder LSTM models which are recurrent neural networks. Our investigations on SMS spam detection demonstrate that the proposed improved spam Converter outperforms all other alternatives regarding accuracy, F1-Score and recall. Additionally, the suggested model performs well on UtkMl's Twitter dataset, suggesting a favorable chance of applying model to other similar issues.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775933","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988094
G. D. Reddy, Yaddanapudi Vssrr Uday Kiran, Prabhdeep Singh, Shubhranshu Singh, Sanchita Shaw, Jitendra Singh
People are concerned about the security of their data over the internet. The data can be protected in many ways to keep unauthorized individuals from accessing it. To secure data, steganography can be used in conjunction with cryptography. It is common for steganography to be used for hiding data or secret messages, whereas cryptography encrypts the messages so that they cannot be read. As a result, the proposed system combines both cryptography and steganography. A steganographic message can be concealed from prying eyes by using an image as a carrier of data. In steganography, writing is done secretly or covertly. The digital steganography algorithm uses text, graphics, and audio as cover media. Due to recent advancements in technology, steganography is challenging to employ to safeguard private data, messages, or digital photographs. This paper presents a new steganography strategy for confidential communications between private parties. A transformation of the ciphertext into an image system is also performed during this process. To implement XOR and ECC (Elliptic Curve Cryptography) encryption, three secure mechanisms were constructed using the least significant bit (LSB). In order to ensure a secure data transmission over web applications, both steganography and cryptography must be used in conjunction. Combined techniques can be used and replace the current security techniques, since there has been an incredible growth in security and awareness among individuals, groups, agencies, and government institutions.
{"title":"A Proficient and secure way of Transmission using Cryptography and Steganography","authors":"G. D. Reddy, Yaddanapudi Vssrr Uday Kiran, Prabhdeep Singh, Shubhranshu Singh, Sanchita Shaw, Jitendra Singh","doi":"10.1109/ICTACS56270.2022.9988094","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988094","url":null,"abstract":"People are concerned about the security of their data over the internet. The data can be protected in many ways to keep unauthorized individuals from accessing it. To secure data, steganography can be used in conjunction with cryptography. It is common for steganography to be used for hiding data or secret messages, whereas cryptography encrypts the messages so that they cannot be read. As a result, the proposed system combines both cryptography and steganography. A steganographic message can be concealed from prying eyes by using an image as a carrier of data. In steganography, writing is done secretly or covertly. The digital steganography algorithm uses text, graphics, and audio as cover media. Due to recent advancements in technology, steganography is challenging to employ to safeguard private data, messages, or digital photographs. This paper presents a new steganography strategy for confidential communications between private parties. A transformation of the ciphertext into an image system is also performed during this process. To implement XOR and ECC (Elliptic Curve Cryptography) encryption, three secure mechanisms were constructed using the least significant bit (LSB). In order to ensure a secure data transmission over web applications, both steganography and cryptography must be used in conjunction. Combined techniques can be used and replace the current security techniques, since there has been an incredible growth in security and awareness among individuals, groups, agencies, and government institutions.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620139","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988523
V. Ghodke, S. S. Pungaiah, M. Shamout, A. A. Sundarraj, Moidul Islam Judder, S. Vijayprasath
In agriculture, automation is an important attribute for improving and enhancing the quality, expansion and efficiency of the products produced. The quality of the rating has been reduced as the product classification has improved. Sorting is one of the most important challenges in the industry, so need a reliable segregation system that allows us to package our products easily and automatically. Features used in this process include pre-processing, entry, division, extraction, classification, and detection. Existing approaches is not accurately finding the fruit result and take more time take to finding the segregation part. To overcome the issue in this work proposed the method Logistic Support Vector Regression (LSVR) is efficient classified the fruits images. Initially start the process include the image dataset, and first step is preprocessing. In this stage, remove unwanted areas of images, to check the imbalanced values and eliminating the image defects. Next step segmenting the images form the stage of preproceeing filtered images, it helps to splitting the images. Extracting the features based on the images weightages and evaluating for classification. Then using the training and testing images for classification, it includes segregating or identifying color, texture, shape, and defects. Finally, classification using LSVR process improves images quality and assists the industry in segregating products. The use of images in the automated packaging process improves the quality of the results in a better way than ever before. Use this approach and smart logistics to keep track of the transaction process. The purpose of this work is primarily to minimize or eliminate waste.
{"title":"Machine Learning for Auto Segregation of Fruits Classification Based Logistic Support Vector Regression","authors":"V. Ghodke, S. S. Pungaiah, M. Shamout, A. A. Sundarraj, Moidul Islam Judder, S. Vijayprasath","doi":"10.1109/ICTACS56270.2022.9988523","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988523","url":null,"abstract":"In agriculture, automation is an important attribute for improving and enhancing the quality, expansion and efficiency of the products produced. The quality of the rating has been reduced as the product classification has improved. Sorting is one of the most important challenges in the industry, so need a reliable segregation system that allows us to package our products easily and automatically. Features used in this process include pre-processing, entry, division, extraction, classification, and detection. Existing approaches is not accurately finding the fruit result and take more time take to finding the segregation part. To overcome the issue in this work proposed the method Logistic Support Vector Regression (LSVR) is efficient classified the fruits images. Initially start the process include the image dataset, and first step is preprocessing. In this stage, remove unwanted areas of images, to check the imbalanced values and eliminating the image defects. Next step segmenting the images form the stage of preproceeing filtered images, it helps to splitting the images. Extracting the features based on the images weightages and evaluating for classification. Then using the training and testing images for classification, it includes segregating or identifying color, texture, shape, and defects. Finally, classification using LSVR process improves images quality and assists the industry in segregating products. The use of images in the automated packaging process improves the quality of the results in a better way than ever before. Use this approach and smart logistics to keep track of the transaction process. The purpose of this work is primarily to minimize or eliminate waste.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392101","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988385
Neha Sharma, Deeksha Kumari
Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers.
{"title":"Voting Classification Approach for Covid-19 Prediction with K-mean and PCA","authors":"Neha Sharma, Deeksha Kumari","doi":"10.1109/ICTACS56270.2022.9988385","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988385","url":null,"abstract":"Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"53 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124300512","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}
This paper presents a triple band antenna integration with a band-pass filter by optimizing the impedance at the interface between the two. A miniaturized antenna is designed at three different frequencies and integrated with a filter and at the output a single frequency of 2.4 GHz is achieved. The filtenna is made-up on FR-4 epoxy substrate and 4.4 is the dielectric constant, and having 1.6mm thickness. The proposed structure is having low cost, miniaturized in size and gives good filtering performance.
{"title":"Antenna-Filter Integration for Wireless Applications","authors":"Shilpam Saxena, Apurva Shrivastava, Sudhanshu Tripathi","doi":"10.1109/ICTACS56270.2022.9988430","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988430","url":null,"abstract":"This paper presents a triple band antenna integration with a band-pass filter by optimizing the impedance at the interface between the two. A miniaturized antenna is designed at three different frequencies and integrated with a filter and at the output a single frequency of 2.4 GHz is achieved. The filtenna is made-up on FR-4 epoxy substrate and 4.4 is the dielectric constant, and having 1.6mm thickness. The proposed structure is having low cost, miniaturized in size and gives good filtering performance.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314970","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9988176
D. R. D. Varma, R. Priyanka
The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.
{"title":"Implementation and Performance Analysis of Novel Support Vector Machine Classifier for Detecting Eye Cancer Image in comparison with Decision Tree","authors":"D. R. D. Varma, R. Priyanka","doi":"10.1109/ICTACS56270.2022.9988176","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9988176","url":null,"abstract":"The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131481299","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 : 2022-10-10DOI: 10.1109/ICTACS56270.2022.9987786
T. V. Kumar, F. V. A. Raj, B. Gopinath, B. Suresh, S. Tamizharasi
Following moving articles alongside their development through video groupings are perhaps of the most essential and most vital undertaking in PC vision. This fills in as the establishment for various more significant level mechanized applications in various spaces, including observation, expanded reality and movement catch in moving item discovery. Object following is key component of an IVS framework which can additionally be demonstrated for some dubious movement identification frameworks. There are numerous approaches and proposed algorithms for object tracking, but the article proposed Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF), which is the basis for the implementation of IVS in this paper. It also derives an equivalent formulation of an SVM model with the circulant matrix expression and presents an effective alternating optimization method for visual tracking. The proposed work characterized to meet following goals: to make a video grouping for moving item following; to plan an exploratory set ready for moving item discovery; and, to plan and carry out moving item following calculation, the proposed calculation was carried out on a caught video succession. Object was identified first as per the picture info, and afterward followed in ensuing casings. The exploratory execution could play out the article following without missing any edge and could effectively overlay bouncing box. It could effectively create a picture grouping after the total execution of Mean Shift Flowchart. The presentation of calculation was checked by effectively following the client characterized object at any climate and playing out the overlay capability in the recognized article.
通过视频分组跟踪移动文章的发展可能是PC视觉中最重要和最重要的工作。这填补了各种更重要的层次机械化应用在各个空间的建立，包括观察，扩展现实和移动物品发现中的运动捕捉。对象跟踪是IVS框架的关键组成部分，它还可以为一些可疑的运动识别框架进行演示。目标跟踪的方法和算法有很多，但本文提出了Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF)，这是本文实现IVS的基础。推导了循环矩阵表示的支持向量机模型的等价表达式，提出了一种有效的视觉跟踪交替优化方法。提出的工作主要实现以下目标:制作一个移动项目跟随的视频分组;为移动项目发现计划一个探索集;为了规划和执行移动项跟随计算，对捕获的视频序列进行了所提出的计算。首先根据图片信息确定物体，然后在随后的弹壳中确定。探索性执行可以在不丢失任何边缘的情况下播放文章，并且可以有效地覆盖弹跳框。它可以在平均移位流程图的总执行后有效地创建一个图片分组。通过在任何气候下有效地跟踪客户特征对象并在识别的文章中发挥覆盖能力来检查计算的呈现。
{"title":"Real-time Visual Detection and Tracking is Implemented in a Clustered Environment using an Adaptive Kernel-Supported Correlation Filter Algorithm","authors":"T. V. Kumar, F. V. A. Raj, B. Gopinath, B. Suresh, S. Tamizharasi","doi":"10.1109/ICTACS56270.2022.9987786","DOIUrl":"https://doi.org/10.1109/ICTACS56270.2022.9987786","url":null,"abstract":"Following moving articles alongside their development through video groupings are perhaps of the most essential and most vital undertaking in PC vision. This fills in as the establishment for various more significant level mechanized applications in various spaces, including observation, expanded reality and movement catch in moving item discovery. Object following is key component of an IVS framework which can additionally be demonstrated for some dubious movement identification frameworks. There are numerous approaches and proposed algorithms for object tracking, but the article proposed Scale Adaptive Kernel Support Correlation Filter Algorithm (SKSCF), which is the basis for the implementation of IVS in this paper. It also derives an equivalent formulation of an SVM model with the circulant matrix expression and presents an effective alternating optimization method for visual tracking. The proposed work characterized to meet following goals: to make a video grouping for moving item following; to plan an exploratory set ready for moving item discovery; and, to plan and carry out moving item following calculation, the proposed calculation was carried out on a caught video succession. Object was identified first as per the picture info, and afterward followed in ensuing casings. The exploratory execution could play out the article following without missing any edge and could effectively overlay bouncing box. It could effectively create a picture grouping after the total execution of Mean Shift Flowchart. The presentation of calculation was checked by effectively following the client characterized object at any climate and playing out the overlay capability in the recognized article.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132094434","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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