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Real-time pedestrian warning system on highway using deep learning methods

机译:基于深度学习方法的高速公路实时行人预警系统

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To lower the traffic accidents in highway systems, it is important to assure the highway be used only by vehicles. If someone accidentally enters the highway without noticing the potential danger, some traffic management system may give out an alarm to the pedestrian or to the nearby vehicles. That can be achieved by modern technology. That is, if the monitoring system or car camera can capture the pedestrian information and immediate give an alarm, obviously it can effectively reduce the incidence of accidents. For this purpose, in this paper, we propose a pedestrian detection algorithm with optimized detection method of region-convolution neural network. It is demonstrated by experiments that the proposed method is able to reach the state-of-the-art methods level. Finally, we implement this algorithm to a real-time monitoring system that could realize pedestrian saliency detection and alarm immediately on the entrance, exits and other important places of highway.
机译:为了降低公路系统中的交通事故,重要的是保证仅由车辆使用的高速公路。如果有人不小心进入高速公路而不注意到潜在的危险,一些交通管理系统可能会向行人或附近车辆发出警报。这可以通过现代技术实现。也就是说,如果监控系统或汽车摄像机可以捕获行人信息并立即发出警报,显然它可以有效降低事故的发生率。为此目的,在本文中,我们提出了一种具有区域卷积神经网络的优化检测方法的行人检测算法。通过实验证明了所提出的方法能够达到最先进的方法水平。最后,我们将该算法实施到实时监控系统,该系统可以在入口,出口和高速公路的其他重要地方立即实现行人显着性检测和警报。

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