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首页> 外文期刊>International Journal of Innovative Computing Information and Control >VEHICLE DETECTION FOR THERMAL VISION-BASED TRAFFIC MONITORING SYSTEM USING PRINCIPAL COMPONENT ANALYSIS
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VEHICLE DETECTION FOR THERMAL VISION-BASED TRAFFIC MONITORING SYSTEM USING PRINCIPAL COMPONENT ANALYSIS

机译：基于主成分分析的基于视觉的交通监控系统的车辆检测

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摘要

Machine vision is a popular technology used in Traffic Monitoring System (TMS) to detect vehicles in the traffic scene. Recently, thermal vision provides an alternative machine vision for the TMS since it demonstrates good vehicle detection accuracy, especially under night condition. Under the thermal vision, the vehicles appear almost similar in the daytime and nighttime, even though the illuminations of the traffic scene are significantly different. This vision effect motivates the development of a single vehicle detection algorithm that works in both of the illumination conditions. It is a challenge that has existed for decades. In this paper, a framework for thermal-vision-based TMS is proposed. Histogram of Oriented Gradients (HoG) is a feature descriptor used to recognize the vehicles on the road. The similar appearance of the vehicles under the thermal vision allows the use of single adaptive reference descriptor in vehicle detection for each lane of the road, in which the reference descriptor is generated and optimized with Principal Component Analysis (PCA). In both the daytime and nighttime, the proposed TMS framework has demonstrated high vehicle detection accuracies under the thermal vision.
机译：机器视觉是交通监控系统（TMS）中用于检测交通场景中车辆的一种流行技术。最近，热视觉为TMS提供了一种替代的机器视觉，因为它具有良好的车辆检测精度，尤其是在夜间条件下。在热视觉的作用下，即使交通场景的照明存在明显差异，车辆在白天和夜间仍显得几乎相似。这种视觉效果促进了在两种照明条件下均可工作的单一车辆检测算法的发展。这是已经存在了几十年的挑战。本文提出了一种基于热视觉的TMS框架。定向梯度直方图（HoG）是用于识别道路上车辆的特征描述符。在热视觉下，车辆的外观相似，因此可以在道路的每个车道的车辆检测中使用单个自适应参考描述符，在其中通过主成分分析（PCA）生成并优化参考描述符。在白天和晚上，建议的TMS框架在热视觉条件下均显示出较高的车辆检测精度。

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