Pub Date : 2020-11-12DOI: 10.1109/rteict49044.2020.9315652
{"title":"RTEICT 2020 Cover Page","authors":"","doi":"10.1109/rteict49044.2020.9315652","DOIUrl":"https://doi.org/10.1109/rteict49044.2020.9315652","url":null,"abstract":"","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122788800","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315623
P. Anand, S. Sumam David, K. Sudeep
This paper evaluates learning-based data-driven models for deblurring of facial images. Existing algorithms for deblurring, when used for facial images, often fail to preserve the facial shape and identity information. The best available models, which are used for general-purpose image deblurring, are pre-trained using only facial images. The Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) and Time to deblur single images are the key metrics used for evaluating the models and for finding the most efficient model for deblurring facial images. From the results, the observation is that even though the PSNR value for DeblurGANv2 model is the highest, the best trade off between PSNR, SSIM, Time to deblur and visual quality is seen in DeblurGAN model.
{"title":"Motion Deblurring of Faces","authors":"P. Anand, S. Sumam David, K. Sudeep","doi":"10.1109/RTEICT49044.2020.9315623","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315623","url":null,"abstract":"This paper evaluates learning-based data-driven models for deblurring of facial images. Existing algorithms for deblurring, when used for facial images, often fail to preserve the facial shape and identity information. The best available models, which are used for general-purpose image deblurring, are pre-trained using only facial images. The Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) and Time to deblur single images are the key metrics used for evaluating the models and for finding the most efficient model for deblurring facial images. From the results, the observation is that even though the PSNR value for DeblurGANv2 model is the highest, the best trade off between PSNR, SSIM, Time to deblur and visual quality is seen in DeblurGAN model.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255402","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315530
K. Karishma Singh, S. Kavya, T. Anupriya, C. Narendra
Electro Static Discharge (ESD) is one of the prime causes of failure of electronic products. To reduce the level of failures caused by ESD, all employees in a manufacturing industry are instructed to wear an ESD safety wear in an Electrostatic Discharge Protected Area (EPA). Thus, this paper proposes a system that can automatically keep a track of the ESD safety wear worn by workers. Using machine learning and deep learning algorithms, a system is developed which can detect ESD safety wear in a real time environment. In this paper, we applied deep learning to multi-class object detection. To implement the object detection module, we used the Single Shot Multibox Detector (SSD) Inception Common Objects in Context (COCO) model for fast and efficient object detection. We have trained the model for 5 object classes (head cap, coat, safety shoe, shoe cover and mask) with 10,000 dataset images. This system can reiterate a warning (voice alert) if some workers are not wearing the above mentioned ESD safety wear appositely. The ability of deep learning to detect the ESD safety wear is studied by conducting experiments using Closed-Circuit Television (CCTV) camera.
静电放电(ESD)是导致电子产品失效的主要原因之一。为了减少由静电放电引起的故障，制造行业的所有员工都被要求在静电放电保护区(EPA)穿着防静电安全服。因此，本文提出了一种能够自动跟踪工人所穿防静电安全防护服的系统。利用机器学习和深度学习算法，开发了一个可以实时检测静电放电安全磨损的系统。在本文中，我们将深度学习应用于多类目标检测。为了实现目标检测模块，我们使用了Single Shot Multibox Detector (SSD) Inception Common Objects in Context (COCO)模型进行快速高效的目标检测。我们用10000张数据集图像训练了5个对象类(头帽、外套、安全鞋、鞋套和口罩)的模型。如果工作人员没有适当佩戴上述防静电安全防护服，该系统可以重复发出警告(语音警报)。通过闭路电视(CCTV)摄像机的实验，研究了深度学习对防静电安全磨损的检测能力。
{"title":"ESD Safety Wear Detection And Voice Alert Using Deep Learning And Embedded System","authors":"K. Karishma Singh, S. Kavya, T. Anupriya, C. Narendra","doi":"10.1109/RTEICT49044.2020.9315530","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315530","url":null,"abstract":"Electro Static Discharge (ESD) is one of the prime causes of failure of electronic products. To reduce the level of failures caused by ESD, all employees in a manufacturing industry are instructed to wear an ESD safety wear in an Electrostatic Discharge Protected Area (EPA). Thus, this paper proposes a system that can automatically keep a track of the ESD safety wear worn by workers. Using machine learning and deep learning algorithms, a system is developed which can detect ESD safety wear in a real time environment. In this paper, we applied deep learning to multi-class object detection. To implement the object detection module, we used the Single Shot Multibox Detector (SSD) Inception Common Objects in Context (COCO) model for fast and efficient object detection. We have trained the model for 5 object classes (head cap, coat, safety shoe, shoe cover and mask) with 10,000 dataset images. This system can reiterate a warning (voice alert) if some workers are not wearing the above mentioned ESD safety wear appositely. The ability of deep learning to detect the ESD safety wear is studied by conducting experiments using Closed-Circuit Television (CCTV) camera.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817675","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315695
M. Raju, T. V. Sudila, V. Gopi, V. Anitha
Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD). Since AD is unlikely to modify its related and intrinsic decay, early diagnosis is crucial, which gives patients a chance to rearrange their lives. Identifying the MCI level is essential, as it assists with further treatment and preliminary steps to control the forward progression towards AD. Brain tissue segmentation is an important aspect of clinical diagnostic tools, yielding excellent results compared to conventional segmentation methods or individual modalities. In this work, the Meta-Heuristic Markov Random Field Segmentation method is used to separate brain tissue, followed by the extraction of intensity, texture, and shape-based features of the segmented Cerebral Spinal Fluid (CSF) and Grey Matter (GM). With the proposed technique, the pre-processing of the input image improves quality and reduces noise. The segmentation is based on multilevel thresholding using Particle Swarm Optimization (PSO) and further improving using Markov Random Field model. Segmented brain tissue (GM and CSF) are used to extract features on shape, intensity, and texture. The four-layer deep neural network is used to classify features. The proposed method is tested with the standard dataset from Alzheimer’s disease-neuro imaging (ADNI). The proposed method achieved a high accuracy rate of 97.5%. A comparison with the previous work yielded results that demonstrate this method’s superiority in terms of the classification of AD and MCI.
{"title":"Classification of Mild Cognitive Impairment and Alzheimer’s Disease from Magnetic Resonance Images using Deep Learning","authors":"M. Raju, T. V. Sudila, V. Gopi, V. Anitha","doi":"10.1109/RTEICT49044.2020.9315695","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315695","url":null,"abstract":"Mild cognitive impairment (MCI) is an early stage of Alzheimer’s disease (AD). Since AD is unlikely to modify its related and intrinsic decay, early diagnosis is crucial, which gives patients a chance to rearrange their lives. Identifying the MCI level is essential, as it assists with further treatment and preliminary steps to control the forward progression towards AD. Brain tissue segmentation is an important aspect of clinical diagnostic tools, yielding excellent results compared to conventional segmentation methods or individual modalities. In this work, the Meta-Heuristic Markov Random Field Segmentation method is used to separate brain tissue, followed by the extraction of intensity, texture, and shape-based features of the segmented Cerebral Spinal Fluid (CSF) and Grey Matter (GM). With the proposed technique, the pre-processing of the input image improves quality and reduces noise. The segmentation is based on multilevel thresholding using Particle Swarm Optimization (PSO) and further improving using Markov Random Field model. Segmented brain tissue (GM and CSF) are used to extract features on shape, intensity, and texture. The four-layer deep neural network is used to classify features. The proposed method is tested with the standard dataset from Alzheimer’s disease-neuro imaging (ADNI). The proposed method achieved a high accuracy rate of 97.5%. A comparison with the previous work yielded results that demonstrate this method’s superiority in terms of the classification of AD and MCI.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130630474","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315549
H. Srinidhi, H. S. Shreenidhi, G. Vishnu
As the population increases and natural resources decrease, the ability to serve humanity with a sufficient amount of food becomes increasingly difficult. The amount of agricultural land decreases proportionally to the increasing population, thus the amount of food produced will decrease significantly, and will be insufficient to serve the growing population. The orthodox methods of farming will not suffice in the near future. Thus, using modern technology and resources, a method of efficient farming must be introduced and employed in the agricultural field. This research makes use of an efficient farming method called hydroponics by adopting machine learning algorithms. The system that has been designed and built, is automated, and uses sensor data to make decisions by using KNN and Lasso Regression algorithm to benefit the crops being grown. With our system we hope to solve the potential food crisis and give everyone access to fresh produce all year round.
{"title":"Smart Hydroponics system integrating with IoT and Machine learning algorithm","authors":"H. Srinidhi, H. S. Shreenidhi, G. Vishnu","doi":"10.1109/RTEICT49044.2020.9315549","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315549","url":null,"abstract":"As the population increases and natural resources decrease, the ability to serve humanity with a sufficient amount of food becomes increasingly difficult. The amount of agricultural land decreases proportionally to the increasing population, thus the amount of food produced will decrease significantly, and will be insufficient to serve the growing population. The orthodox methods of farming will not suffice in the near future. Thus, using modern technology and resources, a method of efficient farming must be introduced and employed in the agricultural field. This research makes use of an efficient farming method called hydroponics by adopting machine learning algorithms. The system that has been designed and built, is automated, and uses sensor data to make decisions by using KNN and Lasso Regression algorithm to benefit the crops being grown. With our system we hope to solve the potential food crisis and give everyone access to fresh produce all year round.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123932033","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315730
Keshavamurthy, Shivashakar, S. Hunagund, S. G. Shiva Prasad Yadav, A. Vinutha
The research aims to be a secured multi-casts transmission for cognitive satellite-terrestrial networks with Multiple input and Multiple Output (MIMO) antenna eavesdroppers where the satellite provides the group of legitimate users with a common confidential message and where interferences from the terrestrial base station (BS) are used to enhance the safety of the satellite link. Idea of this work is to reduce total transmitting power, subject to satellite connectivity secrecy rates and the terrestrial link data rate. The resulting problem of optimization involves the joint optimization of the non-convex and challenging information covariance matrices at the SAT and the BS. In order to turn the non-convex constraints into linear ones, we introduced a successive concave approximation method and proposed an efficient iterative algorithm. The results of the simulation show that the proposed algorithm is superior.
{"title":"Implementation of Secure Multicast Routing for Cognitive Satellite-Terrestrial Networks","authors":"Keshavamurthy, Shivashakar, S. Hunagund, S. G. Shiva Prasad Yadav, A. Vinutha","doi":"10.1109/RTEICT49044.2020.9315730","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315730","url":null,"abstract":"The research aims to be a secured multi-casts transmission for cognitive satellite-terrestrial networks with Multiple input and Multiple Output (MIMO) antenna eavesdroppers where the satellite provides the group of legitimate users with a common confidential message and where interferences from the terrestrial base station (BS) are used to enhance the safety of the satellite link. Idea of this work is to reduce total transmitting power, subject to satellite connectivity secrecy rates and the terrestrial link data rate. The resulting problem of optimization involves the joint optimization of the non-convex and challenging information covariance matrices at the SAT and the BS. In order to turn the non-convex constraints into linear ones, we introduced a successive concave approximation method and proposed an efficient iterative algorithm. The results of the simulation show that the proposed algorithm is superior.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121705329","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315728
G. Somashekhar, H. B. Phaniraju
In this research work the segmentation of the burnt image for the skin is proposed. The main attention of this work is focused on the identification of the burn wounds and the health skin by distinguishing various buns and its depths. In the proposed system the input is collected based on the simple digital image which is collected from the mobile phones or the digital camera. The system collects the texture and colour characteristics observed by the medical staff or the technician for the diagnosis. For segmentation and feature extractions the colour image is converted to ($L^{ast},a^{ast},b^{ast}$) based on the Euclidean distances. After the successful segmentation of the image the features are extracted for the analysis. The system performs an effective clinical demonstration for 100 images with five-fold validation and yields an improve performance and achieves a success rate of 85 %.
{"title":"Segmentation of Burn Area Identification Based on Feature Extraction","authors":"G. Somashekhar, H. B. Phaniraju","doi":"10.1109/RTEICT49044.2020.9315728","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315728","url":null,"abstract":"In this research work the segmentation of the burnt image for the skin is proposed. The main attention of this work is focused on the identification of the burn wounds and the health skin by distinguishing various buns and its depths. In the proposed system the input is collected based on the simple digital image which is collected from the mobile phones or the digital camera. The system collects the texture and colour characteristics observed by the medical staff or the technician for the diagnosis. For segmentation and feature extractions the colour image is converted to ($L^{ast},a^{ast},b^{ast}$) based on the Euclidean distances. After the successful segmentation of the image the features are extracted for the analysis. The system performs an effective clinical demonstration for 100 images with five-fold validation and yields an improve performance and achieves a success rate of 85 %.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148915","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315628
Arvind Kumar, Rampravesh Kumar, K. Kishore
Speech recognition is the ability of devices to respond to spoken commands. In today’s times when we are moving towards an automated world, the area of speech recognition has caught the eye of the researchers. The developments in this area are making waves all around us. Our proposed work presents a method to design a robust digit recognition system in Santhali language using Kaldi toolkit and MATLAB for small vocabulary dataset for varieties of features. Santhali is the most widely spoken local dialect in the state of Jharkhand. Using Kaldi, we trained our system with two training methods; monophone training and triphone training. The triphone method proves to be more efficient than the monophone method because of context mapping. In MATLAB, we obtained 95% accuracy for MFCC+LPC feature extraction applied to a GMM model.
{"title":"Performance analysis of ASR Model for Santhali language on Kaldi and Matlab Toolkit","authors":"Arvind Kumar, Rampravesh Kumar, K. Kishore","doi":"10.1109/RTEICT49044.2020.9315628","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315628","url":null,"abstract":"Speech recognition is the ability of devices to respond to spoken commands. In today’s times when we are moving towards an automated world, the area of speech recognition has caught the eye of the researchers. The developments in this area are making waves all around us. Our proposed work presents a method to design a robust digit recognition system in Santhali language using Kaldi toolkit and MATLAB for small vocabulary dataset for varieties of features. Santhali is the most widely spoken local dialect in the state of Jharkhand. Using Kaldi, we trained our system with two training methods; monophone training and triphone training. The triphone method proves to be more efficient than the monophone method because of context mapping. In MATLAB, we obtained 95% accuracy for MFCC+LPC feature extraction applied to a GMM model.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341268","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315637
R. Priyamvadaa
In today’s data driven world, seamless transfer of data is of paramount importance. The process of data assimilation and transfer demands high accuracy and security. During the outbreak of pandemics like COVID-19, continuous monitoring of temperature and saturation levels by the health officials is necessary. The information exchange between the patient and the health officials should be initiated instantaneously without any delay. For uninterrupted and accelerated data transfer, Message Queuing Telemetry Transport protocol can be deployed. MQTT has demonstrated good mobility, reliability, scalability, interoperability, power saving and security and hence can be considered as an alternative for wireless data transfer during situations wherein social distancing and self-isolation are mandatory.
{"title":"Temperature and Saturation level monitoring system using MQTT for COVID-19","authors":"R. Priyamvadaa","doi":"10.1109/RTEICT49044.2020.9315637","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315637","url":null,"abstract":"In today’s data driven world, seamless transfer of data is of paramount importance. The process of data assimilation and transfer demands high accuracy and security. During the outbreak of pandemics like COVID-19, continuous monitoring of temperature and saturation levels by the health officials is necessary. The information exchange between the patient and the health officials should be initiated instantaneously without any delay. For uninterrupted and accelerated data transfer, Message Queuing Telemetry Transport protocol can be deployed. MQTT has demonstrated good mobility, reliability, scalability, interoperability, power saving and security and hence can be considered as an alternative for wireless data transfer during situations wherein social distancing and self-isolation are mandatory.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127611974","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 : 2020-11-12DOI: 10.1109/RTEICT49044.2020.9315662
G. Poornima, J. Avinash, S. Palle, S. Santosh Kumar, K. S. Sunil Kumar, P. Rajendra Prasad
In recent years there has been increase in development of human pursuing robots which can be used as daily life support robots. The primary goal is to design and fabricate a robot that not only tracks the target but also moves according to it. For implementing this project, a barcode was used as target that robot needs to follow. OpenCV provides an interface to capture live stream with camera. In order to detect the barcode, a program is written in python which is interfaced with OpenCV library. After capturing a video from the camera, it converts it into gray scale video and display it frame by-frame. After detecting the barcode from the video frame, black and white lines of an image is detected and the centroid will be calculated. Based on the position of the centroid, commands are given to the robot to move accordingly. Ultrasonic sensor is used to avoid collision between the robot and obstacles. As a result, the robot pursues the target and it can be used as an assisting system for handicapped people for carrying luggage or can be used in airports, railway stations as a luggage carrier.
{"title":"Image Processing Based Human Pursuing Robot","authors":"G. Poornima, J. Avinash, S. Palle, S. Santosh Kumar, K. S. Sunil Kumar, P. Rajendra Prasad","doi":"10.1109/RTEICT49044.2020.9315662","DOIUrl":"https://doi.org/10.1109/RTEICT49044.2020.9315662","url":null,"abstract":"In recent years there has been increase in development of human pursuing robots which can be used as daily life support robots. The primary goal is to design and fabricate a robot that not only tracks the target but also moves according to it. For implementing this project, a barcode was used as target that robot needs to follow. OpenCV provides an interface to capture live stream with camera. In order to detect the barcode, a program is written in python which is interfaced with OpenCV library. After capturing a video from the camera, it converts it into gray scale video and display it frame by-frame. After detecting the barcode from the video frame, black and white lines of an image is detected and the centroid will be calculated. Based on the position of the centroid, commands are given to the robot to move accordingly. Ultrasonic sensor is used to avoid collision between the robot and obstacles. As a result, the robot pursues the target and it can be used as an assisting system for handicapped people for carrying luggage or can be used in airports, railway stations as a luggage carrier.","PeriodicalId":367246,"journal":{"name":"2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126184762","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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