2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)最新文献_第4页

A YOLO-Based Traffic Counting System 基于yolo的流量计数系统

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00027

Jia-Ping Lin, Min-Te Sun

{"title":"A YOLO-Based Traffic Counting System","authors":"Jia-Ping Lin, Min-Te Sun","doi":"10.1109/TAAI.2018.00027","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00027","url":null,"abstract":"Image recognition can be applied in many applications of Intelligent Transportation System. Through automated traffic flow counting, the traffic information can be presented effectively for a given area. After the existing image recognition model process the monitoring video, the coordinates of objects in each frame can be easily extracted. The extracted object coordinates are then filtered to obtain the required vehicle coordinates. To achieve the function of vehicle counting, it is necessary to identify the relationship of vehicles in different frames, i.e., whether or not they represent the same vehicle. Although the vehicle counting can be achieved by using the tracking algorithm, a short period of recognition failure may cause wrong tracking, which will lead to incorrect traffic counting. In this paper, we propose a system that utilizes the YOLO framework for traffic flow counting. The system architecture consists of three blocks, including the Detector that generates the bounding box of vehicles, the Buffer which stores coordinates of vehicles, and the Counter which is responsible for vehicle counting. The proposed system requires only to utilize simple distance calculations to achieve the purpose of vehicle counting. In addition, by adding checkpoints, the system is able to alleviate the consequence of false detection. The videos from different locations and angles are used to verify and analyze the correctness and overall efficiency of the proposed system, and the results indicate that our system achieves high counting accuracy under the environment with sufficient ambient light.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121616031","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: 57

Weighted Majority Voting with a Heterogeneous System in the Game of Shogi 将棋博弈中异构系统的加权多数投票

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00035

Shogo Takeuchi

引用次数: 1

Interpreting Neural-Network Players for Game 2048 解读Game 2048中的神经网络玩家

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00038

Kiminori Matsuzaki, Madoka Teramura

引用次数: 4

Message from the TAAI 2018 Program Co-Chairs 2018年TAAI项目联合主席致辞

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/taai.2018.00006

Chao-Tung Yang, Chao Chen

引用次数: 0

Tongue Fissure Visualization with Deep Learning 舌裂可视化与深度学习

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00013

Wen-Hsien Chang, H. Chu, Hen-Hong Chang

引用次数: 4

[Copyright notice] (版权)

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/taai.2018.00003

引用次数: 0

Application of Deep Reinforcement Learning in Werewolf Game Agents 深度强化学习在狼人博弈代理中的应用

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00016

Tianhe Wang, Tomoyuki Kaneko

{"title":"Application of Deep Reinforcement Learning in Werewolf Game Agents","authors":"Tianhe Wang, Tomoyuki Kaneko","doi":"10.1109/TAAI.2018.00016","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00016","url":null,"abstract":"Werewolf, also known as Mafia, is a kind of game with imperfect information. Werewolf game agents must cope with two kinds of problems, \"decision on who to trust or to kill\", and \"decision on information exchange\". In this paper, we focus on the first problem. We apply techniques in Deep Q Network in building werewolf agents. We also improve representation of states and actions based on existing agents trained by Q learning method. Our proposed agents were compared with existing agents trained by Q learning method and with existing agents submitted to the AIWolf Contest, the most famous werewolf game agents contest in Japan. For every role, we prepared four agents with proposed method and investigated average win ratio of four agents in our experiments. Experimental results showed that when agents learned and played with the same group of players, our proposed agents have better player performances than existing agents trained by Q learning method and a part of agents submitted to the AIWolf Contest. We obtained promising results by using reinforcement learning method to solve \"decision on who to trust or to kill\" problem without using heuristic methods.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448800","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

Message from the TAAI 2018 General Co-Chairs 2018年TAAI联合主席致辞

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/taai.2018.00005

引用次数: 0

Adaptive Generation of Structured Medical Report Using NER Regarding Deep Learning 基于深度学习的NER自适应生成结构化医疗报告

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/TAAI.2018.00012

Cheng-Tse Wu, Hsiao-ko Chang, Ji-Han Liu, J. Jang

{"title":"Adaptive Generation of Structured Medical Report Using NER Regarding Deep Learning","authors":"Cheng-Tse Wu, Hsiao-ko Chang, Ji-Han Liu, J. Jang","doi":"10.1109/TAAI.2018.00012","DOIUrl":"https://doi.org/10.1109/TAAI.2018.00012","url":null,"abstract":"The structured electronic medical record is the basis for computers to process and achieve the target of precise diagnosis and treatment automatically using the knowledge and features of the techniques such as machine learning and artificial intelligence (AI). Because of the increasing demands on improving the efficiency and the flexibility during the step or phase of classification and extraction, providing the expansion mechanism for the automatic adaption of new NER (Named Entity Recognition, NER) model training during the NER model training stage anytime when the new entities/tags shall be learned and classified and hence the related knowledge database (DB) shall be expanded automatically. The proposed method includes a training stage involving the step of adaptive improved NER model training for the chest x-ray medical reports/files and a test stage involving the step of the dependency parsing and the relation extracting to be perform sequentially, and thus the goals of automatic information extraction and structured medical report generation using the machine learning technique, and the optimization and accuracy improvement of the doctor's work and performance through referring to the structured medical report for diagnosis and treatment can be achieved.","PeriodicalId":211734,"journal":{"name":"2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090923","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

[Title page i] [标题页i]

2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI) Pub Date : 2018-11-01 DOI: 10.1109/taai.2018.00001

引用次数: 0