面向交通拥堵的车辆鲁棒检测及分车道到达累计曲线估计

    Robust Vehicle Detection and Estimation of the Lane-by-Lane Arrival Cumulative Curves for Traffic Congestion

    • 摘要: 针对大交通量拥堵情况下现有视频车辆检测技术不能有效处理车辆相互遮挡而导致的大量漏检问题,提出了一种面向交通拥堵的车辆鲁棒检测及分车道到达累计曲线估计方法.首先,完成非拥堵区域的检测,避免针对交通拥堵停驶车辆进行复杂遮挡处理及检测的工作;然后,基于假设生成和验证框架,融合AdaBoost分类器与车底阴影检测结果,得到车辆鲁棒检测结果;最后,使用投影畸变车辆稳定特征将车辆位置划归特定的车道,准确估计分车道车辆到达累计曲线,实现针对交通检测断面分车道详细交通参数的有效分析.实验结果表明:该方法能够在高峰时段的交通拥堵状态下实时进行车辆鲁棒检测并准确地获取交通参数,有效避免针对车辆遮挡的复杂处理过程,对解决车辆到达率和车头时距调查成本高、工作量大、不确定因素多等问题具有实际的意义.

       

      Abstract: To solve the problem of missing vehicle detection resulted from vehicle occlusion in heavy traffic congestion using video processing technology, a method of robust vehicle detection and estimation of the lane-by-lane arrival cumulative curves for traffic congestion was presented. This method included three aspects:firstly, non-congested areas were detected to avoid the meaningless work of detecting the stopped vehicles with occlusion within traffic congestion. Then, the robust results of vehicle detection were obtained by fusing the AdaBoost vehicle classifier and under vehicle shadow detection based on the hypothesis generation and verification framework. Finally, the lane-by-lane arrival cumulative curves was estimated accurately, after each vehicle was classified into the specific lane by using the stable features without projection distortion, which resulted in the effective analysis with traffic parameters. The experimental results show that this real-time method can detect vehicles robustly and obtain the traffic parameters accurately during the peak period of traffic congestion. It is thus clear that this method can effectively avoid complex implementation dealing with vehicle occlusion, and is significant to solving the practical problems of high cost, heavy workload and many uncertain factors when investigating vehicle arrival rate and headway data.

       

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