2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第9页

Simultaneous Traffic Sign Recognition and Real-Time Communication using Dual Camera in ITS 基于ITS双摄像头的交通标志识别与实时通信

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8668986

Moh. Khalid Hasan, M. Shahjalal, M. Z. Chowdhury, N. Le, Y. Jang

{"title":"Simultaneous Traffic Sign Recognition and Real-Time Communication using Dual Camera in ITS","authors":"Moh. Khalid Hasan, M. Shahjalal, M. Z. Chowdhury, N. Le, Y. Jang","doi":"10.1109/ICAIIC.2019.8668986","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668986","url":null,"abstract":"Research on intelligent transportation system (ITS) is increasing owing to its incredible potentiality in transportation. Numerous features are added in the in-road vehicles facilitating the utilization of newly-developed technologies to reduce traffic collisions and assure human safety. Among them, camera-mounted smart cars are currently very common. These cameras can be used to receive data from light-emitting diodes (LED) of other vehicles, traffic signals or roadside units, which is termed as optical camera communication (OCC). Another significant task that can be performed using the cameras is the automatic recognition of traffic signs. Deep learning algorithms are comprehensively developed for the detection of the LEDs or signs. However, both communication and recognition at the same time is a challenging task as it requires complex image-processing techniques to process the LED and sign images simultaneously. Motivated by this problem, we propose a dual-camera system and an algorithm for communication and recognition at the same time without modifying the current transportation system. Convolutional neural network is used to detect the desired objects primarily. Then one of the cameras is assigned to capture the image frames for further processing of the communication or recognition mechanism. Our algorithm will ensure the reduction of overall computational complexity. At the end of the paper, we enlist the challenges that should be envisaged while considering our algorithm.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128179827","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}

引用次数: 6

Deep Learning Application in Power System with a Case Study on Solar Irradiation Forecasting 深度学习在电力系统中的应用——以太阳辐射预测为例

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8668969

Aslam Muhammad, Jae Myoung Lee, Sugwon Hong, Seung Jae Lee, E. Lee

引用次数: 23

Machine Learning-Based Beamforming in Two-User MISO Interference Channels 基于机器学习的双用户MISO干扰信道波束形成

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669027

H. Kwon, Jung Hoon Lee, Wan Choi

引用次数: 20

Human-Object Contour for Action Recognition with Attentional Multi-modal Fusion Network 基于注意多模态融合网络的动作识别人物轮廓

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669069

Miao Yu, Weizhe Zhang, Qingxiang Zeng, Chao Wang, Jie Li

{"title":"Human-Object Contour for Action Recognition with Attentional Multi-modal Fusion Network","authors":"Miao Yu, Weizhe Zhang, Qingxiang Zeng, Chao Wang, Jie Li","doi":"10.1109/ICAIIC.2019.8669069","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669069","url":null,"abstract":"Human action recognition has great research and application value in intelligent video surveillance, human-computer interaction and other communication fields. In order to improve the accuracy of human action recognition for video understanding, the extraction of human motion features and attentional fusion methods are studied. This paper has two main contributions. Firstly, based on the essence of optical flow validity, a novel dynamic feature expression method called Human-Object Contour(HOC) is presented, which combines object understanding and contextual information. Secondly, referring to the principle of Stacking in ensemble learning, we propose Attentional Multi-modal Fusion Network(AMFN). According to the characteristics of the video, attention is paid to selecting different modalities rather than simple averaging with fixed weight. The experiment shows that HOC is effectively complementary to the static appearance feature, and the accuracy of action recognition with our fusion network improves effectively. Our approach obtains the state-of-the-art performance on the datasets of HMDB51 (72.2%) and UCF101 (96.0%).","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133416022","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}

引用次数: 4

Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing 基于雷达与视觉传感器融合的安全变道车辆路径预测

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669081

Jihun Kim, Ž. Emeršič, D. Han

引用次数: 12

Spectrum Database Construction based on HMM and Spectrum Sharing in Multiple Primary Users Environment 多主用户环境下基于HMM的频谱数据库构建与频谱共享

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669089

Yuya Aoki, T. Fujii

引用次数: 0

Object Management Based on Metadata Registry for Intelligent Mobile Augmented Reality 基于元数据注册的智能移动增强现实对象管理

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669074

S. Jang

引用次数: 1

Deep Learning for Polar Codes over Flat Fading Channels 平坦衰落信道上极点码的深度学习

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669025

A. Irawan, G. Witjaksono, W. Wibowo

引用次数: 8

IT-Business Alignments among Different Divisions of Japanese Corporations 日本公司不同部门之间的it业务结盟

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669032

Michiko Miyamoto

引用次数: 1

Multi-Channel Audio Source Separation Using Azimuth-Frequency Analysis and Convolutional Neural Network 基于方位频率分析和卷积神经网络的多声道音频源分离

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8668841

J. M. Moon, C. Chun, Jun Ho Kim, H. Kim, Tae Kim

{"title":"Multi-Channel Audio Source Separation Using Azimuth-Frequency Analysis and Convolutional Neural Network","authors":"J. M. Moon, C. Chun, Jun Ho Kim, H. Kim, Tae Kim","doi":"10.1109/ICAIIC.2019.8668841","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668841","url":null,"abstract":"Since MPEG-H supports not only channel-based but also object-based audio content, there is a need for a sound source separation technique that converts channel-based to object-based audio. Among the various sound source separation techniques, azimuth-frequency (AF) based sound source separation has been proposed for converting channel-based audio to object-based audio. Unfortunately, it is difficult to set the optimal azimuth and width using this technique. In this paper, we propose a method to determine the optimal azimuth and width based on a convolutional neural network (CNN) classifier. First, depending on numerous azimuths and widths, different sets of audio signals are separated. After that, each audio set is categorized into a specific audio class using the CNN classifier. Then, in order to separate a desired audio signal, the azimuth and width with the highest similarity for a given class are selected. The performance of the CNN classifier is evaluated in terms of separation accuracy and objective measures such as signal-to-distortion ratio (SDR), signal-to-interference ratio (SIR), and signal-to-artifacts ratio (SAR). Consequently, the proposed method provides higher SDR, SAR, SIR, and separation accuracy than a minimum variance distortionless response (MVDR) beamformer as well as a method that only uses AF analysis.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129673807","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}

引用次数: 1