CN113095159B - 一种基于cnn的城市道路交通状况分析方法 - Google Patents
一种基于cnn的城市道路交通状况分析方法 Download PDFInfo
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- CN113095159B CN113095159B CN202110310422.9A CN202110310422A CN113095159B CN 113095159 B CN113095159 B CN 113095159B CN 202110310422 A CN202110310422 A CN 202110310422A CN 113095159 B CN113095159 B CN 113095159B
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- 238000004458 analytical method Methods 0.000 title claims abstract description 17
- 238000001514 detection method Methods 0.000 claims description 18
- 238000011176 pooling Methods 0.000 claims description 15
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- 238000000605 extraction Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
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Publication number | Priority date | Publication date | Assignee | Title |
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CN107730881A (zh) * | 2017-06-13 | 2018-02-23 | 银江股份有限公司 | 基于深度卷积神经网络的交通拥堵视觉检测系统 |
US20210012649A1 (en) * | 2018-03-29 | 2021-01-14 | Nec Corporation | Information processing apparatus, road analysis method, and non-transitory computer readable medium storing program |
CN109147331B (zh) * | 2018-10-11 | 2021-07-27 | 青岛大学 | 一种基于计算机视觉的道路拥堵状态检测方法 |
CN109740463A (zh) * | 2018-12-21 | 2019-05-10 | 沈阳建筑大学 | 一种车载环境下的目标检测方法 |
CN110472467A (zh) * | 2019-04-08 | 2019-11-19 | 江西理工大学 | 基于YOLO v3的针对交通枢纽关键物体的检测方法 |
CN110096981A (zh) * | 2019-04-22 | 2019-08-06 | 长沙千视通智能科技有限公司 | 一种基于深度学习的视频大数据交通场景分析方法 |
CN112257609B (zh) * | 2020-10-23 | 2022-11-04 | 重庆邮电大学 | 一种基于自适应关键点热图的车辆检测方法及装置 |
AU2020103901A4 (en) * | 2020-12-04 | 2021-02-11 | Chongqing Normal University | Image Semantic Segmentation Method Based on Deep Full Convolutional Network and Conditional Random Field |
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Non-Patent Citations (2)
Title |
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一种基于YOLO的交通目标实时检测方法;王思雨;Tanvir Ahmad;;计算机与数字工程(09);全文 * |
基于视频图像分析的地铁列车车辆拥挤度识别方法研究;张杏蔓;鲁工圆;;交通运输工程与信息学报(03);全文 * |
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