CN109766790B - 一种基于自适应特征通道的行人检测方法 - Google Patents
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CN110737968B (zh) * | 2019-09-11 | 2021-03-16 | 北京航空航天大学 | 基于深层次卷积长短记忆网络的人群轨迹预测方法及*** |
CN111523478B (zh) * | 2020-04-24 | 2023-04-28 | 中山大学 | 一种作用于目标检测***的行人图像检测方法 |
CN111767857A (zh) * | 2020-06-30 | 2020-10-13 | 电子科技大学 | 一种基于轻量级两阶段神经网络的行人检测方法 |
CN111884873B (zh) * | 2020-07-13 | 2021-10-29 | 上海华讯网络***有限公司 | 一种自学习的预测网络监控图形的方法和*** |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107301387A (zh) * | 2017-06-16 | 2017-10-27 | 华南理工大学 | 一种基于深度学习的图像高密度人群计数方法 |
CN107330396A (zh) * | 2017-06-28 | 2017-11-07 | 华中科技大学 | 一种基于多属性和多策略融合学习的行人再识别方法 |
EP3261017A1 (en) * | 2016-06-20 | 2017-12-27 | Delphi Technologies, Inc. | Image processing system to detect objects of interest |
CN108229390A (zh) * | 2018-01-02 | 2018-06-29 | 济南中维世纪科技有限公司 | 基于深度学习的快速行人检测方法 |
CN108510012A (zh) * | 2018-05-04 | 2018-09-07 | 四川大学 | 一种基于多尺度特征图的目标快速检测方法 |
CN108564097A (zh) * | 2017-12-05 | 2018-09-21 | 华南理工大学 | 一种基于深度卷积神经网络的多尺度目标检测方法 |
CN109002752A (zh) * | 2018-01-08 | 2018-12-14 | 北京图示科技发展有限公司 | 一种基于深度学习的复杂公共场景快速行人检测方法 |
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US9916522B2 (en) * | 2016-03-11 | 2018-03-13 | Kabushiki Kaisha Toshiba | Training constrained deconvolutional networks for road scene semantic segmentation |
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Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3261017A1 (en) * | 2016-06-20 | 2017-12-27 | Delphi Technologies, Inc. | Image processing system to detect objects of interest |
CN107301387A (zh) * | 2017-06-16 | 2017-10-27 | 华南理工大学 | 一种基于深度学习的图像高密度人群计数方法 |
CN107330396A (zh) * | 2017-06-28 | 2017-11-07 | 华中科技大学 | 一种基于多属性和多策略融合学习的行人再识别方法 |
CN108564097A (zh) * | 2017-12-05 | 2018-09-21 | 华南理工大学 | 一种基于深度卷积神经网络的多尺度目标检测方法 |
CN108229390A (zh) * | 2018-01-02 | 2018-06-29 | 济南中维世纪科技有限公司 | 基于深度学习的快速行人检测方法 |
CN109002752A (zh) * | 2018-01-08 | 2018-12-14 | 北京图示科技发展有限公司 | 一种基于深度学习的复杂公共场景快速行人检测方法 |
CN108510012A (zh) * | 2018-05-04 | 2018-09-07 | 四川大学 | 一种基于多尺度特征图的目标快速检测方法 |
Non-Patent Citations (2)
Title |
---|
Chen Qiaosong ET AL.Scalable Object Detection Using Deep but Lightweight CNN with FeaturesFusion.《International Conference on Image and Graphics》.2017, * |
陈光喜等.基于聚合通道特征及卷积神经网络的行人检测.《计算机工程与设计》.2018, * |
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