Abstract

The work is devoted to the research that was carried out within the framework of computer vision problems applicable to the analysis of images and video information with vehicles. We solve the problem of classifying vehicles. We analyze the drawbacks of Haar features and convolutional neural networks and test the obtained networks using the key point method; we construct an integral algorithm that includes several networks, and we further validate it on a large number of real photographs and types of vehicles. Next, we solve the task to develop a software framework for tracking vehicles by analyzing adjacent photographs from a video sequence. After that, we consider the tracking task in more detail. We analyze modern tracking algorithms using machine learning and describe our implemented tracker with support for the appearance of obstacles between the camera and a moving vehicle. As a result, we propose algorithms and open-source software that, after being configured for specific cameras, can be used in traffic analysis systems.

中文翻译：

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使用照片和视频交通执法相机进行车辆类型识别和车辆跟踪的算法的开发和测试

摘要

这项工作致力于在适用于分析车辆图像和视频信息的计算机视觉问题框架内进行的研究。我们解决了车辆分类的问题。我们分析了 Haar 特征和卷积神经网络的缺点，并使用关键点方法测试得到的网络；我们构建了一个包含多个网络的积分算法，并在大量真实照片和车辆类型上进一步验证。接下来，我们通过分析视频序列中的相邻照片来解决开发用于跟踪车辆的软件框架的任务。之后，我们更详细地考虑跟踪任务。我们使用机器学习分析现代跟踪算法，并描述我们实现的跟踪器，支持相机和移动车辆之间出现障碍物。因此，我们提出算法和开源软件，在针对特定摄像机进行配置后，可用于交通分析系统。