Vehicle detection, counting and classification in various conditions
Vehicle detection, counting and classification in various conditions
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DOI：
10.1049/iet-its.2015.0157
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发表时间：
2016-08-01
影响因子：
2.7
通讯作者：
Safabakhsh, Reza
中科院分区：
文献类型：
--
作者：
Kamkar, Shiva;Safabakhsh, Reza
在过去的几十年里，智能交通系统受到了广泛的关注。车辆检测是该领域的关键任务，车辆计数和分类是两个重要的应用。在本研究中，作者提出了一种车辆检测方法，该方法使用主动基础模型选择车辆并根据其反射对称性对其进行验证。然后，他们通过提取两个特征来对它们进行计数和分类：相应时空图像中的车辆长度以及根据车辆图像在其边界框内的灰度共生矩阵计算出的相关性。训练随机森林将车辆分为三类：小型（例如汽车）、中型（例如货车）和大型（例如公共汽车和卡车）。使用包括七个视频流的数据集对所提出的方法进行评估，这些视频流包含常见的高速公路挑战，例如不同的照明条件、各种天气条件、相机振动和图像模糊。实验结果表明，该方法具有良好的性能及其在白天（有阴影的情况下）、夜间和一年中所有季节的交通监控系统中的使用效率。
Intelligent transportation systems have received a lot of attention in the last decades. Vehicle detection is the key task in this area and vehicle counting and classification are two important applications. In this study, the authors proposed a vehicle detection method which selects vehicles using an active basis model and verifies them according to their reflection symmetry. Then, they count and classify them by extracting two features: vehicle length in the corresponding time-spatial image and the correlation computed from the grey-level co-occurrence matrix of the vehicle image within its bounding box. A random forest is trained to classify vehicles into three categories: small (e.g. car), medium (e.g. van) and large (e.g. bus and truck). The proposed method is evaluated using a dataset including seven video streams which contain common highway challenges such as different lighting conditions, various weather conditions, camera vibration and image blurring. Experimental results show the good performance of the proposed method and its efficiency for use in traffic monitoring systems during the day (in the presence of shadows), night and all seasons of the year.