检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Li, Xuan Wang, Kunfeng Tian, Yonglin Yan, Lan Wang, Fei-Yue School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China Department of Automation University of Science and Technology of China Hefei230027 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

Video image datasets are playing an essential role in design and evaluation of traffic vision algorithms. Nevertheless, a longstanding inconvenience concerning image datasets is that manually collecting and annotating large-scale diversified datasets from real scenes is time-consuming and prone to error. For that virtual datasets have begun to function as a proxy of real datasets. In this paper, we propose to construct large-scale artificial scenes for traffic vision research and generate a new virtual dataset called "parallelEye". First of all, the street map data is used to build 3D scene model of Zhongguancun Area, Beijing. Then, the computer graphics, virtual reality, and rule modeling technologies are utilized to synthesize large-scale, realistic virtual urban traffic scenes, in which the fidelity and geography match the real world well. Furthermore, the Unity3D platform is used to render the artificial scenes and generate accurate ground-truth labels, e.g., semantic/instance segmentation, object bounding box, object tracking, optical flow, and depth. The environmental conditions in artificial scenes can be controlled completely. As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research. The experimental results demonstrate that this pipeline is able to generate photorealistic virtual datasets with low modeling time and high accuracy labeling. Copyright © 2017, The Authors. All rights reserved.

关键词： Pipelines

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Mutual clustering on comparative texts via heterogeneous information networks

arXiv

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arXiv 2019年

作者： Cao, Jianping Wang, Senzhang Wen, Danyan Peng, Zhaohui Yu, Philip S. Wang, Fei-yue Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Ministry of Industry and Information Technology Nanjing211106 China School of Economics & Management Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science University of Illinois at Chicago Chicago60607 United States Institute for Data Science Tsinghua University Beijing100084 China Beijing China Qingdao Academy of Intelligent Industries Qingdao266000 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be "comparative" since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared "anchor texts" to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Erratum: Analysis of opinion spreading in homogeneous networks with signed relationships

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Journal of Statistical Mechanics: Theory and Experiment 2012年第10期2012卷

作者： Pengyi Fan Hui Wang Pei Li Wei Li Zhihong Jiang Research Center of Computational Experiments and Parallel System Technology College of Information Systems and Management National University of Defense Technology Changsha 410073 People's Republic of China

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Scene-specific pedestrian detection based on parallel vision

arXiv

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arXiv 2017年

作者： Zhang, Wenwen Wang, Kunfeng Qu, Hua Zhao, Jihong Wang, Fei-Yue School of Software Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Communication and Information Engineering Xi’an Jiaotong University Xi’an710049 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes. While labeling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be labeled. What’s more, the labeling information is not so accurate in the pixels manually and different people make different labeling information. In this paper, we propose an ACP-based method, with augmented reality’s help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking labeled data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes. Copyright © 2017, The Authors. All rights reserved.

关键词： Virtual reality

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Training and testing object detectors with virtual images

arXiv

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arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named parallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining parallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Decision-Making in Driver-Automation Shared Control: A Review and Perspectives

arXiv

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arXiv 2020年

作者： Wang, Wenshuo Na, Xiaoxiang Cao, Dongpu Gong, Jianwei Xi, Junqiang Xing, Yang Wang, Fei-Yue CA94720 United States Department of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave. West Waterloo ONN2L3G1 Canada Department of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control. The human driver, as an essential agent in the driver-vehicle shared control systems, should be precisely modeled regarding their cognitive processes, control strategies, and decision-making processes. The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans. Many open-ended questions arise, such as what proper role of human drivers should act in a shared control scheme? How to make an intelligent decision capable of balancing the benefits of agents in shared control systems? Due to the advent of these attentions and questions, it is desirable to present a survey on the decision-making between human drivers and highly automated vehicles, to understand their architectures, human driver modeling, and interaction strategies under the driver-vehicle shared schemes. Finally, we give a further discussion on the key future challenges and opportunities. They are likely to shape new potential research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision making

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