随着智慧城市建设的不断深入，大量的传感器设备铺置在城市公路和轨道等交通场景，为多维度全方位感知城市交通状态构建了广泛的感知网络，产生了海量的交通视频数据。海量交通视频数据是城市管理的数据宝藏，理解与分析这些数据是智慧城市建设的关键。面对高度冗余的交通视频数据，如何高效准确地挖掘和提取结构化信息，实现对重点目标（如人、车、物）的快速检测、识别与检索，是交通视频处理的核心问题——交通视频结构化分析。交通视频结构化分析包括车辆视频结构化分析、人员结构化分析及其行为分析。其中，车辆结构化作为一个复杂的多步骤任务，主要由车辆的检测、车辆的属性（车牌、车型和颜色）识别以及车辆的检索和重识别等子任务构成。人脸结构化和行人结构化是交通视频中行人结构化智能分析中的两个重要研究方向，主要分析人脸或者行人的一些表观属性。行人行为分析是指对行人在复杂交通环境下做出的动作进行识别和预测。本文从交通视频中的车辆、行人及其行为分析等方面，阐述交通视频结构化分析领域的研究热点及前沿进展，汇总比较国内外的相关成果，并对交通视频结构化分析领域的研究进行总结分析与展望。

As the construction of smart cities continues to deepen, our country gradually builds multidimensional and omnidirectional sensor systems in roads, railways, and urban rails and other ground transportation fields to build strong data support for smart transportation. Faced with all-weather traffic data collected by sensors, analyzing the data by relying solely on human resources is no longer possible. Therefore, studying the structural analysis technology of traffic video and establishing a safe, flexible, and efficient intelligent transportation system has significant social benefits and application value. Traffic video structural analysis is the core technology in smart transportation. It aims to use artificial intelligence algorithms to parse unstructured traffic video data into structured semantic information that is easy for workers and computers to understand and provide basic technical support for subsequent related tasks. The structural analysis of traffic video is a key technology for smart city construction. It can help the police in quickly locating criminal vehicles and travel routes, greatly improve the police’s efficiency in solving crimes, and maintain city safety; it can also automatically identify illegal vehicles and types of violation, constrains people to abide by the traffic order, and realize a smooth urban traffic environment. With the advent of the 5G internet of things era, ultrahigh network bandwidth and transmission speed further improves the quality and efficiency of vehicle video transmission. Efficiently and accurately conducting traffic video structure analysis will be the focus of research in the next few years. Traffic video structural analysis includes vehicle video structural analysis, personnel structural analysis, and behavior analysis. Among them, as a complex, multistep task, vehicle structuring is mainly composed of three subtasks, namely, vehicle detection, vehicle attributes (license plate, type, and color) recognition, and vehicle retrieval and reidentification. Human face structuring and pedestrian structuring are two important research directions in the intelligent analysis of traffic videos. They mainly analyze some apparent attributes of human faces or pedestrians, such as age, gender, mask, backpack, clothing color, and length. Pedestrian behavior analysis refers to the identification and prediction of pedestrian actions. For example, the speed at which pedestrians currently head and in which direction, whether they are answering calls, and whether they have to cross the road. For the task of vehicle structure analysis, first, the object detection technology must be used to quickly and accurately locate the vehicle. Second, on the basis of positioning the vehicle, it fully excavates the visual characteristics of the vehicle, realizes the 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