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首页> 外文会议>2013 Seventh International Conference on Distributed Smart Cameras >Road traffic tracking and parameter estimation based on visual information analysis using self-calibrated camera views
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Road traffic tracking and parameter estimation based on visual information analysis using self-calibrated camera views

机译：基于视觉信息分析的道路交通跟踪和参数估计（使用自校准摄像机视图）

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Traffic safety has drawn increasing interest for researchers lately. Monitoring road traffic, tracking vehicles on roads and analysis surrounding vehicle information from an ego vehicle can be used to help the improvement of traffic safety and also provide drivers with surrounding vehicle information and giving warning signs on risky situations. In this paper, we propose a novel road traffic tracking and analysis scheme based on visual information with self-calibrated camera views from a single camera. In the proposed method, vehicles entering the scene are tracked by using combination of detection and tracking. A reference model containing multiple instance of candidate vehicles as well as a negative set of background samples are used for the detection and tracking, where similarity of each candidate object is compared. To analyze the parameters related to dynamic vehicles, a novel method is proposed for camera self-calibration where a priori information is exploited. This enables mapping tracked vehicles in 2D images to the ground plane in the 3D coordinate system where parameters such as trajectory, speed, distance and density of vehicles may be accurately estimated. The main novelties of the proposed scheme lie in the combined tracking and detection, new formulae derived in camera selfcalibration, and the hybrid system that handles both daytime and nighttime scenarios. Experiments were performed on several videos containing two way traffic and road crossing, preliminary results and evaluation show that the proposed scheme is rather promising for such an application.
机译：最近，交通安全引起了研究人员的越来越多的兴趣。监视道路交通，跟踪道路上的车辆并分析来自自我车辆的周围车辆信息可用于帮助改善交通安全，还可为驾驶员提供周围车辆信息并在危险情况下发出警告信号。在本文中，我们提出了一种基于视觉信息的新型道路交通跟踪和分析方案，该视觉信息具有来自单个摄像机的自校准摄像机视图。在提出的方法中，结合检测和跟踪对进入场景的车辆进行跟踪。包含候选车辆的多个实例以及背景样本的否定集合的参考模型用于检测和跟踪，其中比较每个候选对象的相似性。为了分析与动态车辆相关的参数，提出了一种利用先验信息进行摄像机自标定的新方法。这使得能够将2D图像中的追踪到的车辆映射到3D坐标系中的地平面，在该3D坐标系中，可以准确估计车辆的轨迹，速度，距离和密度等参数。该方案的主要新颖之处在于组合的跟踪和检测，通过相机自校准得出的新公式以及可同时处理白天和晚上场景的混合系统。对包含双向交通和道路交叉口的多个视频进行了实验，初步结果和评估表明，该方案对于此类应用具有很大的前景。

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