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

Xin He, Delu Zeng
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引用次数: 9

Abstract

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 ofhighway.
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基于深度学习方法的高速公路实时行人预警系统
为了降低公路系统中的交通事故,保证公路只供车辆使用是很重要的。如果有人在没有注意到潜在危险的情况下不小心进入高速公路,一些交通管理系统可能会向行人或附近的车辆发出警报。这可以通过现代技术来实现。也就是说,如果监控系统或车载摄像头能够捕捉到行人信息并立即报警,显然可以有效地减少事故的发生。为此,本文提出了一种基于区域卷积神经网络优化检测方法的行人检测算法。实验结果表明,所提出的方法能够达到最先进的方法水平。最后,我们将该算法应用到一个实时监控系统中,该系统可以在高速公路的出入口等重要场所实现行人显著性检测和即时报警。
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