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平行视觉:基于ACP的智能视觉计算方法

王坤峰 苟超 王飞跃

王坤峰, 苟超, 王飞跃. 平行视觉:基于ACP的智能视觉计算方法. 自动化学报, 2016, 42(10): 1490-1500. doi: 10.16383/j.aas.2016.c160604
引用本文: 王坤峰, 苟超, 王飞跃. 平行视觉:基于ACP的智能视觉计算方法. 自动化学报, 2016, 42(10): 1490-1500. doi: 10.16383/j.aas.2016.c160604
WANG Kun-Feng, GOU Chao, WANG Fei-Yue. Parallel Vision: An ACP-based Approach to Intelligent Vision Computing. ACTA AUTOMATICA SINICA, 2016, 42(10): 1490-1500. doi: 10.16383/j.aas.2016.c160604
Citation: WANG Kun-Feng, GOU Chao, WANG Fei-Yue. Parallel Vision: An ACP-based Approach to Intelligent Vision Computing. ACTA AUTOMATICA SINICA, 2016, 42(10): 1490-1500. doi: 10.16383/j.aas.2016.c160604

平行视觉:基于ACP的智能视觉计算方法

doi: 10.16383/j.aas.2016.c160604
基金项目: 

国家自然科学基金 61304200

国家自然科学基金 61533019

详细信息
    作者简介:

    王坤峰  中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.2008年获得中国科学院研究生院博士学位.主要研究方向为智能交通系统, 智能视觉计算, 机器学习.E-mail:kunfeng.wang@ia.ac.cn

    苟超  中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.2012年获得电子科技大学学士学位.主要研究方向为智能交通系统, 图像处理, 模式识别.E-mail:gouchao2012@ia.ac.cn

    通讯作者:

    王飞跃  中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科学技术大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn

Parallel Vision: An ACP-based Approach to Intelligent Vision Computing

Funds: 

National Natural Science Foundation of China 61304200

National Natural Science Foundation of China 61533019

More Information
    Author Bio:

     Associate professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from the Graduate University of Chinese Academy of Sciences in 2008. His research interest covers intelligent transportation systems, intelligent vision computing, and machine learning.

     Ph. D. candidate at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree from the University of Electronic Science and Technology of China in 2012. His research interest covers intelligent transportation systems, image processing, and pattern recognition.

    Corresponding author: WANG Fei-Yue  Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology. His research interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper
  • 摘要: 在视觉计算研究中,对复杂环境的适应能力通常决定了算法能否实际应用,已经成为该领域的研究焦点之一.由人工社会(Artificial societies)、计算实验(Computational experiments)、平行执行(Parallel execution)构成的ACP理论在复杂系统建模与调控中发挥着重要作用.本文将ACP理论引入智能视觉计算领域,提出平行视觉的基本框架与关键技术.平行视觉利用人工场景来模拟和表示复杂挑战的实际场景,通过计算实验进行各种视觉模型的训练与评估,最后借助平行执行来在线优化视觉系统,实现对复杂环境的智能感知与理解.这一虚实互动的视觉计算方法结合了计算机图形学、虚拟现实、机器学习、知识自动化等技术,是视觉系统走向应用的有效途径和自然选择.
  • 图  1  虚拟火车站的平面图[26](包括站台和火车轨道(左)、主候车室(中)和购物商场(右).该摄像机网络包括16台虚拟摄像机)

    Fig.  1  Plan view of the virtual train station[26] (Revealing the concourses and train tracks (left), the main waiting room (middle), and the shopping arcade (right). An example camera network comprising 16 virtual cameras is illustrated.)

    图  2  虚拟KITTI数据集[32] (上: KITTI多目标跟踪数据集中的一帧图像; 中:虚拟KITTI数据集中对应的图像帧, 叠加了被跟踪目标的标注边框; 下:自动标注的光流(左)、语义分割(中)和深度(右))

    Fig.  2  The virtual KITTI dataset[32]. (Top: a frame of a video from the KITTI multi-object tracking benchmark. Middle: the corresponding synthetic frame from the virtual KITTI dataset with automatic tracking ground truth bounding boxes. Bottom: automatically generated ground truth for optical flow (left), semantic segmentation (middle), and depth (right).)

    图  3  SYNTHIA数据集[34] (左:人工场景中的一帧图像; 中:对应的语义标记; 右:虚拟城市的全貌)

    Fig.  3  The SYNTHIA dataset[34] (A sample frame (left) with its semantic labels (middle) and a general view of the virtual city (right).)

    图  4  RenderCar中的样本图像[35]

    Fig.  4  Sample images from RenderCar[35]

    图  5  平行视觉的基本框架与体系结构

    Fig.  5  Basic framework and architecture for parallel vision

    图  6  货车的3D模型样例

    Fig.  6  Sample 3D models of trucks

    图  7  Faster R-CNN的结构图[15

    Fig.  7  Flowchart of Faster R-CNN[15

    表  1  人工室外场景的构成要素

    Table  1  Components for artificial outdoor scenes

    场景要素 内容
    静态物体 建筑物、天空、道路、人行道、围墙、植物、立柱、交通标志、路面标线等
    动态物体 汽车(轿车、货车、公交车)、自行车、摩托车、行人等
    季节 春、夏、秋、冬
    天气 晴、阴、雨、雪、雾、霾等
    光源 太阳、路灯、车灯等
    下载: 导出CSV
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