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Received：19 January 2021，
Revised：2021-2-4，
Accepted：11 February 2021，
Published：16 June 2021
移动端阅览
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 identification of the inherent attributes of vehicle
and generates structured tags about the vehicle. Finally
on the basis of structured tags
the retrieval technology and reidentification technology are further combined to realize the retrieval and reidentification of a specific vehicle in the massive video data. Personnel structural analysis and behavior analysis can detect and identify pedestrians in traffic videos and conduct structured data extraction and behavior analysis of detected personnel. In the analysis of personnel structure
a person is extracted as a descriptive individual. In terms of face structure
it includes accurate facial positioning
facial feature extraction
and facial feature comparison. In terms of pedestrian structure
it includes gender
age
and age of the person. Various descriptive information includes height
hair accessories
clothing
carrying items
and walking patterns. Pedestrian behavior analysis is carried out on the basis of personnel structure analysis. Behavior analysis refers to the recognition
comprehension
and prediction of pedestrian actions. In the area of big data processing and analysis of traffic video
research on vehicle structuring started earlier and related technologies have also developed rapidly
but it can still be remarkably developed. The premise of vehicle structuring is vehicle detection
which is affected by the shooting scene and the moving speed of the vehicle. Accurately locating the vehicle in the case of low light and the fast vehicle speed is still a problem to be solved. Many types of vehicles are found in the market
and the differences between models of similar brands are small. License plate recognition has become more important. In complex and changeable scenes
the generalization and accuracy of the positioning and recognition algorithm should be further improved. The extensive deployment of traffic monitoring equipment realizes all-weather monitoring of relevant road systems and further increases the difficulty of vehicle retrieval and reidentification tasks. Rapid retrieval or reidentification of target vehicles in complex and changeable scenes is crucial. It requires continuous investment and a much innovative research by scientific researchers. The need for structured pedestrian analysis has gradually emerged with further improvement of urban management. Pedestrian structuring mainly analyzes some apparent attributes of faces or pedestrians
such as age
gender
and clothing style
and provides more detailed data support for subsequent related tasks. Pedestrian structured analysis technology has also ushered in a period of rapid development with the development of deep learning. However
the structured analysis of pedestrians for specific scenarios
such as accurately identifying the age and gender of a person in an unconstrained environment
implementing the deployment of high-precision models in terminal systems with limited resources
and integrating multimodal information to further improve the accuracy of pedestrian attribute recognition
needs further research. Pedestrian behavior analysis is a more advanced task in traffic video big data processing and analysis. It is more challenging due to factors
such as shooting scenes
moving cameras
viewing angles
and lighting changes. Judging from the behavior recognition effect of the mainstream neural network architecture
the current model does not achieve the desired effect on the large-scale behavior data set Kinetic because the existing model still fails to fully learn and model the behavioral timing relationship. In the field of behavior recognition
future research can still focus on recognition models for designing long-time-dependent network architectures
adapting large-scale data sets
and achieving lightweight behavior. With the development of Internet of Things and 5G technologies
the promotion of new technologies has also played an important role in the structural analysis of traffic video. To be equipped with IoT devices has become an inevitable trend for modern cars. Vehicles can be connected to basic transportation facilities (vehicle to infrastructure
V2I) or to surrounding vehicles (vehicle-to-vehicle
V2V). The development of these technologies depends on the common progress of vehicle video structuring and internet of things technology. With the global popularity of 5G technology
rapid transmission of high-quality video data has become a reality. Extracting structured information more efficiently from traffic videos
such as vehicle information
pedestrian information
and behavior prediction
has become more urgent. Researchers should study on improving the performance of related algorithms
should design more efficient hardware systems
and build more efficient traffic video structured analysis systems through software and hardware collaborations. We discuss the related work on traffic video structural analysis in detail from three aspects
as follows: vehicle
personnel
and behavior analysis. Moreover
we summarize these research works and provide some reasonable directions for future work.
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