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1. 发明申请

US20060274917A1 Image processing techniques for a video based traffic monitoring system and methods therefor 有权
标题翻译：基于视频的流量监控系统的图像处理技术及其方法
公开(公告)号：US20060274917A1
公开(公告)日：2006-12-07
申请号：US11504276
申请日：2006-08-15
申请人： Yew Liam Ng , Kim Siah Ang , Chee Chung Chong , Ming Kun Gu
发明人： Yew Liam Ng , Kim Siah Ang , Chee Chung Chong , Ming Kun Gu
IPC分类号： G06K9/00
CPC分类号： G06K9/00785 , G06K9/3241 , G06K2209/23 , G08G1/0104 , G08G1/04 , H04N7/18
摘要： The present disclosure relates to a number of inventions directed, generally, to the application of image processing techniques to traffic data acquisition using video images. The inventions reside in a traffic monitoring system, the basic function of which is for traffic data acquisition and incident detection. More specifically, the application of image processing techniques for the detection of vehicle, from sequence of video images, as well as the acquisition of traffic data and detection of traffic incident. In one aspect, the present invention provides a method of processing images received from a video based traffic monitoring system. In another aspect, the present invention is directed to a Region Of Interest (ROI) for detection of a moving vehicle and a further aspect is directed to a method of detecting day or night status in a traffic monitoring system. It's The application of various algorithms to a video based traffic monitoring system is also considered inventive. Other inventive aspects of the present traffic monitoring system are outlined in the claims.
摘要翻译：本公开涉及通常涉及应用图像处理技术以使用视频图像来交通数据采集的许多发明。本发明位于交通监控系统中，其基本功能用于交通数据采集和事件检测。更具体地，应用图像处理技术来检测车辆，从视频图像序列，以及获取交通数据和检测交通事故。一方面，本发明提供一种处理从基于视频的业务监控系统接收的图像的方法。另一方面，本发明涉及用于检测移动车辆的感兴趣区域（ROI），并且另一方面涉及一种在交通监控系统中检测白天或夜间状态的方法。它是基于视频的流量监控系统的各种算法的应用也被认为是创新的。在权利要求中概述了本交通监控系统的其它创造性方面。

2. 发明授权

US07460691B2 Image processing techniques for a video based traffic monitoring system and methods therefor 有权
标题翻译：基于视频的流量监控系统的图像处理技术及其方法
公开(公告)号：US07460691B2
公开(公告)日：2008-12-02
申请号：US11504276
申请日：2006-08-15
申请人： Yew Liam Ng , Kim Siah Ang , Chee Chung Chong , Ming Kun Gu
发明人： Yew Liam Ng , Kim Siah Ang , Chee Chung Chong , Ming Kun Gu
IPC分类号： G06K9/00
CPC分类号： G06K9/00785 , G06K9/3241 , G06K2209/23 , G08G1/0104 , G08G1/04 , H04N7/18
摘要： The present disclosure relates to a number of inventions directed, generally, to the application of image processing techniques to traffic data acquisition using video images. The inventions reside in a traffic monitoring system, the basic function of which is for traffic data acquisition and incident detection. More specifically, the application of image processing techniques for the detection of vehicle, from sequence of video images, as well as the acquisition of traffic data and detection of traffic incident. In one aspect, the present invention provides a method of processing images received from a video based traffic monitoring system. In another aspect, the present invention is directed to a Region Of Interest (ROI) for detection of a moving vehicle and a further aspect is directed to a method of detecting day or night status in a traffic monitoring system. It's The application of various algorithms to a video based traffic monitoring system is also considered inventive. Other inventive aspects of the present traffic monitoring system are outlined in the claims.
摘要翻译：本公开涉及通常涉及应用图像处理技术以使用视频图像来交通数据采集的许多发明。本发明位于交通监控系统中，其基本功能用于交通数据采集和事件检测。更具体地，应用图像处理技术来检测车辆，从视频图像序列，以及获取交通数据和检测交通事故。一方面，本发明提供一种处理从基于视频的业务监控系统接收的图像的方法。在另一方面，本发明涉及用于检测移动车辆的感兴趣区域（ROI），并且另一方面涉及一种在交通监控系统中检测日间或夜间状态的方法。它是基于视频的流量监控系统的各种算法的应用也被认为是创新的。在权利要求中概述了本交通监控系统的其它创造性方面。

3. 发明授权

US06470261B1 Automatic freeway incident detection system and method using artificial neural network and genetic algorithms 有权
标题翻译：自动高速公路事件检测系统和使用人工神经网络和遗传算法的方法
公开(公告)号：US06470261B1
公开(公告)日：2002-10-22
申请号：US09743992
申请日：2001-01-16
申请人： Yew Liam Ng , Kim Chwee Ng
发明人： Yew Liam Ng , Kim Chwee Ng
IPC分类号： G06G776
CPC分类号： G06K9/00785 , G06N3/086
摘要： Design of a neural network for automatic detection of incidents on a freeway is described. A neural network is trained using a combination of both back-propagation and genetic algorithm-based methods for optimizing the design of the neural network. The back-propagation and genetic algorithm work together in a collaborative manner in the neural network design. The training starts with incremental learning based on the instantaneous error and the global total error is accumulated for batch updating at the end of the training data being presented to the neural network. The genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then use the analyzed results to breed new neural networks that tend to be better suited to the problems at hand.
摘要翻译：描述了用于自动检测高速公路事件的神经网络的设计。使用反向传播和基于遗传算法的方法的组合来训练神经网络以优化神经网络的设计。反向传播和遗传算法在神经网络设计中以协作的方式一起工作。训练从基于瞬时误差的增量学习开始，并且在向神经网络呈现的训练数据结束时累积用于批量更新的全局总误差。遗传算法直接评估多组神经网络的并行性能，然后使用分析结果来培育更适合手头问题的新型神经网络。

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