Pub Date : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301932
ChauChung Song, Chih-Heng Wang, Yifeng Yang
In recent years, the development of precision agriculture is a new technology. The main reason for the automation of agricultural processes is to save the time and energy required to perform repeated farming tasks and to increase production by treating each crop separately and applying smart agricultural concepts. In this paper, an automatic detection and image recognition of citrus diseases is presented that can help farmer find the disease and identify it from the captured images. This method use YOLO(You Only Look Once) algorithm which is an object detection model to detect and recognize the diseases from citrus leaf images. YOLO can realtime detect the disease and circle around it on the image and video. The dataset includes images of citrus leaf with two kinds of diseases: Citrus Canker, Citrus Greening.
精准农业是近年来发展起来的一门新技术。农业过程自动化的主要原因是为了节省执行重复耕作任务所需的时间和精力，并通过分别处理每种作物和应用智能农业概念来提高产量。本文提出了一种柑橘病害的自动检测与图像识别方法，可以帮助农民从采集的图像中发现病害并进行识别。该方法采用YOLO(You Only Look Once)算法作为目标检测模型，对柑橘叶片图像中的病害进行检测和识别。YOLO可以实时检测疾病，并在图像和视频上绕圈。该数据集包括柑橘叶片的两种病害图像:柑橘溃疡病和柑橘绿化。
{"title":"Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases","authors":"ChauChung Song, Chih-Heng Wang, Yifeng Yang","doi":"10.1109/ECICE50847.2020.9301932","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301932","url":null,"abstract":"In recent years, the development of precision agriculture is a new technology. The main reason for the automation of agricultural processes is to save the time and energy required to perform repeated farming tasks and to increase production by treating each crop separately and applying smart agricultural concepts. In this paper, an automatic detection and image recognition of citrus diseases is presented that can help farmer find the disease and identify it from the captured images. This method use YOLO(You Only Look Once) algorithm which is an object detection model to detect and recognize the diseases from citrus leaf images. YOLO can realtime detect the disease and circle around it on the image and video. The dataset includes images of citrus leaf with two kinds of diseases: Citrus Canker, Citrus Greening.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126616737","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9302023
Zhi-Yao Xu, Jinn-Feng Jiang, Hung-Yuan Wei, K. Hsu
To link with reality sensors and virtual scenes, this research uses the sensor simulation software PreScan as the test platform with the vehicle’s hardware-in-the-loop (HIL). Simulation on vehicle auxiliary systems and sensors is effective and efficient in a virtual road environment. To reduce costs, various sensors are tested for the future development of Ethernet AVB. The data transmission rate above 100Mbit/s is achieved and multiple car lenses and display screens are supported. Retaining high reliability of industrial-grade Ethernet satisfies car manufacturers’ demand and Tier 1 safety requirements of original equipment function. As the ADAS is equipped with multiple camera lenses, the high-speed ethernet AVB acts as an important data converging channel. The main processor of a vehicle system calculates them in real time. Image sensing data provides advanced ADAS functions such as surround view, obstacle recognition and lane deviation warning. PreScan can speed up the development process of an autonomous vehicle system.
{"title":"Driving Simulation of Autonomous Vehicle with ADS Control","authors":"Zhi-Yao Xu, Jinn-Feng Jiang, Hung-Yuan Wei, K. Hsu","doi":"10.1109/ECICE50847.2020.9302023","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9302023","url":null,"abstract":"To link with reality sensors and virtual scenes, this research uses the sensor simulation software PreScan as the test platform with the vehicle’s hardware-in-the-loop (HIL). Simulation on vehicle auxiliary systems and sensors is effective and efficient in a virtual road environment. To reduce costs, various sensors are tested for the future development of Ethernet AVB. The data transmission rate above 100Mbit/s is achieved and multiple car lenses and display screens are supported. Retaining high reliability of industrial-grade Ethernet satisfies car manufacturers’ demand and Tier 1 safety requirements of original equipment function. As the ADAS is equipped with multiple camera lenses, the high-speed ethernet AVB acts as an important data converging channel. The main processor of a vehicle system calculates them in real time. Image sensing data provides advanced ADAS functions such as surround view, obstacle recognition and lane deviation warning. PreScan can speed up the development process of an autonomous vehicle system.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127401386","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9302014
Zhang-Bin Chen, Yang Liu
With the development of AI technology, face recognition technology is more accurate and faster than before. The application of the technology in smart hotels fully integrates the management and services for a guest room system or leisure of entertainment purposes, which is realized by "face". The face recognition system provides relevant technical support for smart hotels and allows visitors to pass quickly, facilitate the management, and enjoy the convenience of high technology. The article introduces the concept of a smart hotel, artificial intelligence technology, face recognition technology and key algorithms, comprehensive application of cloud computing, big data, AI (artificial intelligence), Internet of Things, and other emerging technologies. From the perspective of application to the smart hotel, research on human faces is performed for the application of recognition technology. It provides a technical reference for the construction of smart hotels.
{"title":"Application of Face Recognition in Smart Hotels","authors":"Zhang-Bin Chen, Yang Liu","doi":"10.1109/ECICE50847.2020.9302014","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9302014","url":null,"abstract":"With the development of AI technology, face recognition technology is more accurate and faster than before. The application of the technology in smart hotels fully integrates the management and services for a guest room system or leisure of entertainment purposes, which is realized by \"face\". The face recognition system provides relevant technical support for smart hotels and allows visitors to pass quickly, facilitate the management, and enjoy the convenience of high technology. The article introduces the concept of a smart hotel, artificial intelligence technology, face recognition technology and key algorithms, comprehensive application of cloud computing, big data, AI (artificial intelligence), Internet of Things, and other emerging technologies. From the perspective of application to the smart hotel, research on human faces is performed for the application of recognition technology. It provides a technical reference for the construction of smart hotels.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128671690","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301934
Chih-Fang Huang, Cheng-Yuan Huang
Music emotion is important for listeners’ cognition. With the rapid development of technology, the variety of music has become more diverse and spread faster. However, the cost of music production is still very high. To solve the problem, the AI music composition has gradually gained attention in recent years. The purpose of this study is to establish an automated composition system that includes music, emotions, and machine learning. The system includes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. The subjects listen to the results of the system and judge that music corresponds to the original emotion.
{"title":"Emotion-based AI Music Generation System with CVAE-GAN","authors":"Chih-Fang Huang, Cheng-Yuan Huang","doi":"10.1109/ECICE50847.2020.9301934","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301934","url":null,"abstract":"Music emotion is important for listeners’ cognition. With the rapid development of technology, the variety of music has become more diverse and spread faster. However, the cost of music production is still very high. To solve the problem, the AI music composition has gradually gained attention in recent years. The purpose of this study is to establish an automated composition system that includes music, emotions, and machine learning. The system includes the music database with emotional tags as input, and deep learning trains the CVAE-GAN model as the framework to produce the music segments corresponding to the specified emotions. The subjects listen to the results of the system and judge that music corresponds to the original emotion.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147572","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301979
K. G. Medha Nag, Sharvari, D. Vaishnavi, S. Rajashree, Prasad B. Honnavalli
The sensors used in storage warehouses contain sensitive information hence if intercepted by miscreants, the security of the warehouse can be threatened. Thus, it is important to encrypt the data before sending it. Rivest Shamir Adleman - RSA has been used for encryption of smart sensor data. Transmission Control Protocol with Protocol Data Unit (TCP/IP with PDU) has been used for its transmission. The network has been simulated using Cisco Packet Tracer.
仓库中使用的传感器含有敏感信息，一旦被不法分子截获，仓库的安全就会受到威胁。因此，在发送数据之前对其进行加密是很重要的。Rivest Shamir Adleman - RSA已用于智能传感器数据的加密。TCP/IP带PDU (Transmission Control Protocol with Protocol Data Unit)用于其传输。利用Cisco Packet Tracer对该网络进行了仿真。
{"title":"RSA Implementation on Sensor Data in Cold Storage Warehouse","authors":"K. G. Medha Nag, Sharvari, D. Vaishnavi, S. Rajashree, Prasad B. Honnavalli","doi":"10.1109/ECICE50847.2020.9301979","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301979","url":null,"abstract":"The sensors used in storage warehouses contain sensitive information hence if intercepted by miscreants, the security of the warehouse can be threatened. Thus, it is important to encrypt the data before sending it. Rivest Shamir Adleman - RSA has been used for encryption of smart sensor data. Transmission Control Protocol with Protocol Data Unit (TCP/IP with PDU) has been used for its transmission. The network has been simulated using Cisco Packet Tracer.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122306440","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 research develops an automated multi-angle identification and gripping path planning method of a robotic arm. First, we integrate a 3D camera to obtain the image, position, and distance of the workpiece and then send the image to the remote host via a network connection to identify the workpiece and calculation path with a deep learning algorithm. Through the process, the best path and the angle of arm movement are found. The experimental results show that the system continuously reads real-time images from the 3D camera and performs the calculations to correct the moving path when the arm moves. The overall operation is very smooth, and the workpiece can be accurately clamped.
{"title":"3D Cameras and Algorithms for Multi-Angle Gripping and Control of Robotic Arm","authors":"Ching-Ying Yeh, Zheng-Han Shi, Jieh-Tsyr Chuang, Kai-Hsun Hsu, Shang-Wei Liu, Ruo-Wei Wu, Ching-Hsiang Yang, Nian-Ze Hu, Jeng-Dao Lee","doi":"10.1109/ECICE50847.2020.9301931","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301931","url":null,"abstract":"This research develops an automated multi-angle identification and gripping path planning method of a robotic arm. First, we integrate a 3D camera to obtain the image, position, and distance of the workpiece and then send the image to the remote host via a network connection to identify the workpiece and calculation path with a deep learning algorithm. Through the process, the best path and the angle of arm movement are found. The experimental results show that the system continuously reads real-time images from the 3D camera and performs the calculations to correct the moving path when the arm moves. The overall operation is very smooth, and the workpiece can be accurately clamped.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114135781","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301994
Hanas Subakti, Hui-Sung Liang, Jehn-Ruey Jiang
We propose an indoor localization method using FPFE (Fingerprint Feature Extraction) with Bluetooth Low Energy (BLE) beacon fingerprints. FPFE apples either AE or PCA to extract features of beacon fingerprints and then measures the similarity between the features using the concept of the Minkowski distance. FPFE selects k RPs with the k smallest Minkowski distances for estimating the position of the target device. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that the FPFE achieves an average error of 0.68 m which is better than those of other related BLE fingerprint-based localization methods.
{"title":"Indoor Localization with Fingerprint Feature Extraction","authors":"Hanas Subakti, Hui-Sung Liang, Jehn-Ruey Jiang","doi":"10.1109/ECICE50847.2020.9301994","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301994","url":null,"abstract":"We propose an indoor localization method using FPFE (Fingerprint Feature Extraction) with Bluetooth Low Energy (BLE) beacon fingerprints. FPFE apples either AE or PCA to extract features of beacon fingerprints and then measures the similarity between the features using the concept of the Minkowski distance. FPFE selects k RPs with the k smallest Minkowski distances for estimating the position of the target device. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that the FPFE achieves an average error of 0.68 m which is better than those of other related BLE fingerprint-based localization methods.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128623620","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301992
Hong-Yi Pai
Recently, job instruction training (JIT) has widely used mobile learning (M-learning) in undertakings, such as technical maintenance and other collaborative fields. Emerging new media, including mixed reality, has influenced the way people learn through its adaptive learning algorithm. With the concept of self-directed learning, M-learning may be a better choice for JIT when an enterprise has no available instructor and minimal operating profits. The M-learning system derives from Malcolm Knowles’ theory [2] of andragogy and constructs multimedia instruction tools that result in suitable game-based learning. With its practical and easy-to-use function, the system focuses on the simulation training of installing and disassembling the A330 Airbus brakes system, for example. The development process shows how one can effectively use adaptive learning in the mobile devices’ interactive design when following the ADDIE model. In this study, we analyze the user experience’s validity to realize both the advantages and disadvantages of mobile learning in augmented reality. We demonstrate how the learning system improves through the instruction methods, animation display, and interactive design to achieve the target of assimilating and consolidating knowledge. We hope that this study increases the efficiency of job instruction training in Taiwanese companies. We have developed this research in three phases: 1) Research consolidation on the development of M-learning strategies within the theory of andragogy; 2) The improvement of visual perception through technology aids in M-learning, and 3) The comparative analysis of the characteristics of multimedia interfaces.
{"title":"Mixed Reality of Augmented Reality in Mobile Learning for Aircraft Maintenance","authors":"Hong-Yi Pai","doi":"10.1109/ECICE50847.2020.9301992","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301992","url":null,"abstract":"Recently, job instruction training (JIT) has widely used mobile learning (M-learning) in undertakings, such as technical maintenance and other collaborative fields. Emerging new media, including mixed reality, has influenced the way people learn through its adaptive learning algorithm. With the concept of self-directed learning, M-learning may be a better choice for JIT when an enterprise has no available instructor and minimal operating profits. The M-learning system derives from Malcolm Knowles’ theory [2] of andragogy and constructs multimedia instruction tools that result in suitable game-based learning. With its practical and easy-to-use function, the system focuses on the simulation training of installing and disassembling the A330 Airbus brakes system, for example. The development process shows how one can effectively use adaptive learning in the mobile devices’ interactive design when following the ADDIE model. In this study, we analyze the user experience’s validity to realize both the advantages and disadvantages of mobile learning in augmented reality. We demonstrate how the learning system improves through the instruction methods, animation display, and interactive design to achieve the target of assimilating and consolidating knowledge. We hope that this study increases the efficiency of job instruction training in Taiwanese companies. We have developed this research in three phases: 1) Research consolidation on the development of M-learning strategies within the theory of andragogy; 2) The improvement of visual perception through technology aids in M-learning, and 3) The comparative analysis of the characteristics of multimedia interfaces.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127576882","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301953
Chih-Wei Wu, Jian-Jiun Ding
A novel comprehensive detail refinement network, called the CDRNet, to learn robust and diverse features from vehicle images is proposed. There are three modules in the proposed algorithm: the global attention, the detail, and the local feature refinement modules. The global attention module extracts crucial global characteristics while the detail and local refinement modules retrieve important minor features. Experiments on benchmark datasets, VeRi-776 and VehicleID, show that the proposed network outperforms state-of-the-art approaches and is very helpful for vehicle re-identification.
{"title":"Comprehensive Detail Refinement Network for Vehicle Re-identification","authors":"Chih-Wei Wu, Jian-Jiun Ding","doi":"10.1109/ECICE50847.2020.9301953","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301953","url":null,"abstract":"A novel comprehensive detail refinement network, called the CDRNet, to learn robust and diverse features from vehicle images is proposed. There are three modules in the proposed algorithm: the global attention, the detail, and the local feature refinement modules. The global attention module extracts crucial global characteristics while the detail and local refinement modules retrieve important minor features. Experiments on benchmark datasets, VeRi-776 and VehicleID, show that the proposed network outperforms state-of-the-art approaches and is very helpful for vehicle re-identification.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128042728","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 : 2020-10-23DOI: 10.1109/ECICE50847.2020.9301976
Pei-Yun Hsu, Mei Lin Huang, H. Chiang
A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.
{"title":"Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model","authors":"Pei-Yun Hsu, Mei Lin Huang, H. Chiang","doi":"10.1109/ECICE50847.2020.9301976","DOIUrl":"https://doi.org/10.1109/ECICE50847.2020.9301976","url":null,"abstract":"A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.","PeriodicalId":130143,"journal":{"name":"2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":"605 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916962","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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