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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems
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Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems

机译：智能交通系统视频目标检测与识别的关键算法

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摘要

With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.
机译：随着视频检测和识别系统的推广和视频图像处理技术的推进，基于计算机视觉技术的智能交通系统的应用研究得到了越来越多的关注。它全面利用图像处理，模式识别，人工智能等技术。它还涉及处理和分析由检测系统收集的视频图像序列，智能地了解视频内容和制作处理，以及处理各种问题，例如事故信息判断，行人和车辆分类，交通流量参数检测和移动目标跟踪等各种问题。它促进智能交通系统更智能，实用，为交通管理和控制提供全面的实时交通状态信息。因此，基于计算机视觉的交通信息检测方法研究具有重要的理论和实践意义。视频目标的检测和识别是智能运输和计算机视野领域的重要研究方向。然而，由于背景复杂性，照明改变，目标闭塞等因素在检测和识别环境中，应用仍然面临许多困难，并且需要进一步改善鲁棒性和识别的鲁棒性和准确性。在本文中，研究了视频对象检测和识别中的几个关键问题，包括目标和背景的精确分割，卷影在复杂的场景中;精确分类提取的前景目标;复杂背景中的目标识别。为了应对这些问题，本文提出了相应的解决方案。

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