KEYWORDS: Action recognition, Pose estimation, Design and modelling, Video, Education and training, Data modeling, Video surveillance, Deep learning, Safety, Neural networks
The detection and recognition of pedestrian abnormal behavior have been currently attractive research hotspots in surveillance video content-analysis. However, due to the complexity and diversity of pedestrian abnormal behavior, the accurate and efficient behavior-recognition remains a challenging issue in the field of pedestrian safety supported by computer vision technologies. Previous studies mostly focused on detecting and locating abnormal behavior from given videos rather than recognizing pedestrian behavior, which may be incompetent in a few practical scenarios since the threat of each anomaly is needed to be evaluated. As individual pedestrian behaviors are closely related to human postures, in this study, we propose a novel experiment design approach for pedestrian abnormal-behavior recognition based on pose estimation and behavior recognition. We first classify and annotate the common abnormal behaviors in public crowds’ abnormal-behavior datasets, such as ShanghaiTech and CUHK Avenue. Then, the pose estimation toolbox, Openpose, is employed to extract skeleton sequences of behaviors annotated. Finally, the skeleton sequences are classified by the spatial-temporal graph convolutional network (ST-GCN) to implement behavior recognition. This proposed experiment design-approach can provide a technical support to validate the mathematic models or algorithms about pedestrian abnormal behavior recognition.
In recent years, pedestrian stampede accidents in cross-passages of large public places occurred frequently, as an inevitable issue in the area of public security. Abnormal behaviors is one of factors leading to pedestrian stampedes. To recognize behaviors, pedestrian pose recognition is introduced as a hot research topic in the fields of deep learning and computer vision, and has developed into a vibrant research field with various real-world applications, such as human-computer interaction, animation, 3D reconstruction, and abnormal behavior detection. This study reviews literatures related with human pose recognition to pedestrian merging areas such as cross-passage in public places. By detecting abnormal pedestrian behaviors such as falling or crowding, potential accidents in densely crowded areas, such as stampedes, can be prevented, thereby improving public safety. Then, we systematically introduce the methods of pedestrian pose recognition in cross-passage in public places from the perspective of pose estimation and behavior recognition, focusing on the research progress and advantages and disadvantages of each recognition method. Finally, the outlook on challenging issues and future development trends of the pose recognition research is drawn out.
KEYWORDS: Safety, Information security, Non-line-of-sight propagation, Signal attenuation, Fire, Environmental sensing, Algorithm development, Signal detection, Ranging, Power consumption
With the rise of Internet of Things (IoT) technology, the technical requirements for indoor positioning technology are gradually growing. Indoor high-precision positioning technology is obtaining widespread attention from industrial areas, especially the field of safety management for hazardous operators. Ultra-wideband (UWB) technology has become one of research focuses for indoor positioning technology research because of its outperformances treating with multipath resistance, interference resistance, owning high penetration and high accuracy. With the improvements of the management standards, enterprises increasingly need precise positioning in personnel security management, movement track and activity area restrictions. To outline the current development status of the state-of-art in positioning technologies, this study introduces the development and algorithms of UWB positioning technology and related applications in the fields of personnel security, providing a comprehensive reference for personnel positioning and security monitoring. Finally, we introduce an effective positioning approach based on a hand-ring UWB tag, which is capable of real-time monitoring of life safety indicators, such as heart rate and blood oxygen. This hand-ring UWB tag can monitor the both position and human-body healthy status, and will be widely used in various fields in the future.
In public traffic scenarios, the old pedestrians, as the vulnerable and care-needing people, often have serious and irreversible effects on their bodies once accidents such as falls occur in their lives. To recognize the dangerous falling behavior of old pedestrians in public transportation buildings, this paper analyzes the gait frequency characteristics of the old pedestrians, tracks specific old pedestrians, adopts the kinematic stability theory, and obtains the kinematic characteristics of video image information based on computer vision technology. Finally, this study builds a kinematics model for the recognition of the fall behavior of the old pedestrians, which can provide an invisible safety guarantee for the safe travel of the old pedestrians.
In the high-density crowd flow places in public buildings, typical mobile obstacles, such as trolley cases, mobile sweeping trolleys, shuttle trolleys, police patrol cars, etc., carried by passengers bring convenience for passengers to travel, and can also act as typical obstacles that hinder the flow of people. It is easy to block the flow of people, cause the crowd to become unstable, and cause overcrowding and even stampede accidents. To study the influence of moving obstacles on crowd stability, this paper analyzes the spatial and moving characteristics of typical moving obstacles and constructs a motion model of moving obstacles. Furthermore, based on smooth particle hydrodynamics (SPH), a coupled macroscopic pedestrian flow model including moving obstacles and pedestrian flow is proposed. In order to verify the effectiveness of this proposed coupled motion model, this study takes trolley luggage as an example to design and implement a moving obstacle experiment in pedestrian flow, exploring the impact of moving obstacles to the pedestrian flow, further to study the stability of pedestrian flow.
In public places, the fall behaviors of pedestrians possibly lead to the disturbances of the crowd, and even cause stampedes. This study deals with the problem of identifying anomalous pedestrian behavior in an effort to stop potential stampedes in public areas. In order to detect the fall behavior of pedestrians in public places, Baidu AI was introduced in this paper to detect key skeleton points of pedestrians in a single frame sourced from surveillance videos. The ratio of human height to width and cotangent value of the direction angle of the pedestrian minimum peripheral rectangle are selected as feature vectors. Fall behavior detection model based on SVM is proposed. Experiments are designed and implemented to validate the proposed fall behavior detection model in this paper. This study can provide technical support for early warning and prevention of possible stampede accidents in public places.
Experiment design and implement to detect the possible pedestrian abnormal-behaviors in cross passages of public buildings are more significant to prevent possible crowd accidents than ever before. The further support of abnormal-behavior experiments can be helpful to stability analysis of moving pedestrian crowds. To summarize the experiments on pedestrian abnormal behavior detection based on computer vision technology, this study focuses both on the abnormal behaviors of moving pedestrians in public traffic areas and the computer vision technologies. A 3D scene analysis workflow using computer vision for crowd behavior experiment is designed. The Workflow model of abnormal behavior recognition and stability analysis in crowd movement used in experiment design is proposed based on Lyapunov criterion theory. Finally, a survey table of typical abnormal behaviors in public scenes is figured out.
To meet to the requirements of the national production safety law of China, large manufacturing enterprises need to implement the sampling of dynamic number of employees in key areas, emergency evacuation drill and emergency evacuation organization. Aiming at the existing problems such as high randomness of overtime work of workshop employees, frequent worker aggregation phenomena for short meetings, fast aggregation, short time and high risk of dining employees in the canteens, uneven personnel distribution and crowded entrances and exits in the conference center, this paper analyzes the psychological and moving behavior characteristics of personnel in these typical scenarios, and introduces the convolution neural network (CNN) model in the field of machine vision, to build a multi-scenario employee number statistics and density estimation model for factory workshops, canteens and large meeting centers. Further, according to the density-risk relationship, a crowd aggregation risk early warning model is established. Finally, taking the video surveillance system (VSS) as the data acquisition source, the application cases of practical scenes such as workshop, canteen and meeting center are designed to verify the effectiveness of the density estimation model and aggregation risk early warning model proposed in this paper. Thereby this paper provides technical guarantee for the safety of employees in large manufacturing enterprises.
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