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摘要翻译：
由于交通流的动态性，准确预测交叉口交通信号持续时间是一个具有挑战性的问题。尽管可以使用监督学习，但参数可能会在不同的巷道交界处发生变化。在本文中，我们提出了一个计算机视觉引导专家系统，该系统可以学习给定交通路口的离场率，该模型采用传统的排队论模型。首先，利用Dirichlet过程混合模型(DPMM)对运动车辆的光流进行时间分组。这些组被称为轨迹或时间簇。然后使用轨道特征来学习交通路口的动态行为，特别是在信号的开/关周期期间。与其他常用的跟踪特征相比，本文提出的基于排队论的跟踪方法可以更准确地预测下一个周期的信号开放持续时间。该假设在两个公开的视频数据集上得到了验证。结果表明，基于DPMM的特征比现有的跟踪框架更好地估计$\mu$。该方法可用于城市和高速公路交叉口智能交通控制系统的设计。
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英文标题：
《Queuing Theory Guided Intelligent Traffic Scheduling through Video
Analysis using Dirichlet Process Mixture Model》
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作者：
Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
Accurate prediction of traffic signal duration for roadway junction is a challenging problem due to the dynamic nature of traffic flows. Though supervised learning can be used, parameters may vary across roadway junctions. In this paper, we present a computer vision guided expert system that can learn the departure rate of a given traffic junction modeled using traditional queuing theory. First, we temporally group the optical flow of the moving vehicles using Dirichlet Process Mixture Model (DPMM). These groups are referred to as tracklets or temporal clusters. Tracklet features are then used to learn the dynamic behavior of a traffic junction, especially during on/off cycles of a signal. The proposed queuing theory based approach can predict the signal open duration for the next cycle with higher accuracy when compared with other popular features used for tracking. The hypothesis has been verified on two publicly available video datasets. The results reveal that the DPMM based features are better than existing tracking frameworks to estimate $\mu$. Thus, signal duration prediction is more accurate when tested on these datasets.The method can be used for designing intelligent operator-independent traffic control systems for roadway junctions at cities and highways.
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PDF链接：
https://arxiv.org/pdf/1803.0648
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