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An automatic road sign recognition system based on a computational model of human recognition processing

机译：基于人类识别处理计算模型的自动路标识别系统

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This paper presents an automatic road sign detection and recognition system that is based on a computational model of human visual recognition processing. Road signs are typically placed either by the roadside or above roads. They provide important information for guiding, warning, or regulating the behaviors drivers in order to make driving safer and easier. The proposed recognition system is motivated by human recognition processing. The system consists of three major components: sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts the spatial and temporal information of interest from video sequences. The extracted information then serves as the input stimuli to a spatiotemporal attentional (STA) neural network in the perceptual analyzer. If stimulation continues, focuses of attention will be established in the neural network. Potential features of road signs are then extracted from the image areas corresponding to the focuses of attention. The extracted features are next fed into the conceptual analyzer. The conceptual analyzer is composed of two modules: a category module and an object module. The former uses a configurable adaptive resonance theory (CART) neural network to determine the category of the input stimuli, whereas the later uses a configurable heteroassociative memory (CHAM) neural network to recognize an object in the determined category of objects. The proposed computational model has been used to develop a system for automatically detecting and recognizing road signs from sequences of traffic images. The experimental results revealed both the feasibility of the proposed computational model and the robustness of the developed road sign detection system. (C) 2004 Elsevier Inc. All rights reserved.
机译：本文提出了一种基于人类视觉识别处理的计算模型的自动路标检测和识别系统。道路标志通常放置在路边或道路上方。它们为指导，警告或调节驾驶员的行为提供了重要的信息，以使驾驶更安全，更轻松。所提出的识别系统是由人类识别处理驱动的。该系统由三个主要组件组成：感官，知觉和概念分析仪。感官分析器从视频序列中提取感兴趣的空间和时间信息。然后，提取的信息将作为感知分析器中时空注意（STA）神经网络的输入刺激。如果继续刺激，将在神经网络中建立注意力焦点。然后从与关注焦点相对应的图像区域中提取路标的潜在特征。接下来，将提取的特征输入到概念分析器中。概念分析器由两个模块组成：类别模块和对象模块。前者使用可配置的自适应共振理论（CART）神经网络来确定输入刺激的类别，而后者使用可配置的异联想记忆（CHAM）神经网络来识别所确定的对象类别中的对象。所提出的计算模型已被用于开发一种系统，该系统可以从交通图像序列中自动检测和识别道路标志。实验结果揭示了所提出的计算模型的可行性以及所开发路标检测系统的鲁棒性。 （C）2004 Elsevier Inc.保留所有权利。

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