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Traffic State Recognition of Intersection Based on Image Model and PCA Hashing
Journal of Advanced Transportation ( IF 2.0 ) Pub Date : 2020-06-15 , DOI: 10.1155/2020/3828395
Li-li Zhang 1, 2 , Li Wang 2 , Qi Zhao 2
Affiliation  

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

中文翻译:

基于图像模型和PCA散列的交叉口交通状态识别

实施有效的交通控制策略的前提是准确的交通状态识别。在现有研究中,通过使用统计特征和现场交通数据的长期规模检测来处理交通状态识别方法。因此,动态特性和交通流量的细微变化很容易被忽略。目前,越来越多的先进交通检测技术提供了可靠,准确的数据,以测量和区分城市道路交通状况,例如协作式车辆基础设施系统,广域雷达技术和5G技术。这项研究提出了一种称为HTSI(高精度交通状态识别方法)的新方法,该方法基于交叉路口交通状态识别中的先进检测技术:用于路口交通状态识别的原始数据是具有跟踪特性的高精度检测数据,这使该数据看起来像是从上帝的角度看路口的图片。为此,我们构建了相交的图像模型,并以不同于传统图像处理的方式实现了图像特征提取。然后,将十字路口的交通状态识别问题转换为带有标签的图像搜索问题。图像搜索是通过哈希算法实现的。最后,综合实验证明,该方法比其他方法更准确,更精细。我们构建了相交的图像模型,并以不同于传统图像处理的方式实现了图像特征提取。然后,将十字路口的交通状态识别问题转换为带有标签的图像搜索问题。图像搜索是通过哈希算法实现的。最后,综合实验证明,该方法比其他方法更准确,更精细。我们构建了一个相交的图像模型,并以不同于传统图像处理的方式实现了图像特征提取。然后,将十字路口的交通状态识别问题转换为带有标签的图像搜索问题。图像搜索是通过哈希算法实现的。最后,综合实验证明,该方法比其他方法更准确,更精细。
更新日期:2020-06-15
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