基于计算机视觉的电梯轿厢智能监控系统的研究与设计基于计算机视觉的电梯轿厢智能监控系统的研究与设计摘要为了保障人们的生命财产安全，电梯轿厢监控系统逐渐变成一个重要的场所监控领域。
本文提出了一种基于计算机视觉的电梯轿厢智能监控系统，该系统可以实现电梯轿厢内的目标检测、跟踪、异常行为检测和人脸识别等功能，有效地提高了电梯轿厢的安全性和管理水平。
本文首先介绍了电梯轿厢监控系统的研究背景和现状。
然后，针对目标检测和跟踪问题，提出了基于深度学习的目标检测器YOLOv3和基于卡尔曼滤波的目标跟踪算法。
针对异常行为检测问题，提出了基于深度学习的行为识别模型和基于分类器的异常行为检测方法。
针对人脸识别问题，提出了基于深度学习的人脸检测和识别模型。
最后，本文设计和实现了一个基于计算机视觉的电梯轿厢智能监控系统原型。
该系统采用了基于深度学习的目标检测器YOLOv3、基于卡尔曼滤波的目标跟踪算法、基于行为识别模型的异常行为检测和基于深度学习的人脸检测和识别模型等技术。
实验结果表明：本系统可以有效地实现电梯轿厢内人员目标检测、跟踪、异常行为检测和人脸识别等功能，具有较高的实用性和可靠性。
关键词：电梯轿厢，计算机视觉，目标检测，目标跟踪，异常行为检测，人脸识别AbstractIn order to ensure the safety of people's lives and property, the elevator car monitoring system has gradually become an important place monitoring field. This paper proposes a computer vision-basedintelligent monitoring system for elevator cars, which can realize functions such as target detection, tracking, abnormal behavior detection and face recognition in the elevator car, effectively improving the safety and management level of the elevator car.This paper first introduces the research backgroundand status of the elevator car monitoring system. Then, for the target detection and tracking problems, a deep learning-based target detector YOLOv3 and a Kalmanfilter-based target tracking algorithm are proposed. For the abnormal behavior detection problem, abehavior recognition model based on deep learning and a classifier-based abnormal behavior detection method are proposed. For the face recognition problem, a deep learning-based face detection and recognition model isproposed.Finally, this paper designs and implements a prototype of a computer vision-based intelligent monitoring system for elevator cars. The system adopts technologies such as a deep learning-based target detector YOLOv3, a Kalman filter-based target tracking algorithm, an abnormal behavior detection based on behavior recognition model, and a deep learning-based face detection and recognition model. Experimental results show that the system can effectively achieve functions such as target detection, tracking, abnormal behavior detection and face recognition of personnelin the elevator car, and has high practicality and reliability.Keywords: elevator car, computer vision, target detection, target tracking, abnormal behavior detection, face recognitioThe proposed computer vision system for elevator cars is composed of multiple modules including target detection, target tracking, abnormal behavior detection, and face recognition. The system utilizes a lightweight and efficient target detection algorithm, which can quickly detect and locate the elevator passengers. The target tracking module uses advancedalgorithms to track the movement of individuals in the elevator car, even in complex situations with occlusions and overlap.To detect abnormal behavior of passengers, the behavior recognition model is introduced. This model recognizes a set of predefined behaviors, including aggressive behavior, suspicious behavior, and wandering behavior, and triggers an alarm when such behaviors are detected. This greatly enhances the security of the elevator car and helps to prevent potential safety hazards.The face detection and recognition module of the system adopts deep learning-based algorithms, which are highly accurate and efficient for identifying individuals in real-time. By comparing the captured image with the database of recorded faces, the system can accurately identify individuals and provide necessary access control.Experimental results show that the proposed computer vision system has high accuracy in target detection, tracking, abnormal behavior recognition, and face recognition of personnel in the elevator car. The system is also highly practical, and has strong reliability in real-world scenarios.In conclusion, the proposed computer vision system offers great potential for improving safety and security in elevator cars. The system can alert the security personnel in real-time and help prevent dangerous situations. This technology opens up new possibilities for smart elevator systems, which can further enhance passenger safety and comfortOne potential application of this technology is in high-rise buildings where elevators are a critical means of transport for people and goods. Often such buildings have restricted access and strict security protocols to prevent unauthorized entry. The proposed system can help the security personnel to automatically identify people who are authorized to access the building and flag any suspicious activity. This can help prevent unauthorized access and prevent security breaches.Another potential benefit of this technology is in improving the comfort and convenience of passengers. The system can automatically recognize frequently used floors and pre-select them for the passengers. This can significantly reduce waiting times and improve the overall efficiency of the elevator system. Additionally, the system can also track elevator usagepatterns and optimize the elevator service based on the usage data. For example, if the system noticesthat a particular elevator is highly used during peak hours, it can reroute the elevator to the floor with the highest demand.Overall, the proposed computer vision system has the potential to revolutionize the way we interact with elevators. It can improve the safety, security, and comfort of passengers while also offering significant benefits to building owners and operators. With continued research and development, this technology can help pave the way for smart elevator systems that are highly efficient, safe, and reliableOne potential application for computer vision in elevators is in the area of predictive maintenance. By constantly monitoring the elevator's components and systems, the computer vision system can detect any potential issues or malfunctions before they become serious problems. This can help prevent unexpected breakdowns and reduce downtime, which is especially important in high-traffic areas such as office buildings, hospitals, and shopping centers.Another potential benefit of computer vision in elevators is in the area of accessibility. Elevatorscan be equipped with sensors and cameras that detect the presence of passengers and adjust the elevator's behavior accordingly. For example, if a passenger with a mobility impairment enters the elevator, the computer vision system can detect their presence and automatically adjust the elevator's speed, acceleration, and braking to ensure a smooth and comfortable ride.Finally, computer vision can also be used to improve the overall user experience of elevators. For example, the system can detect passenger preferences and adjust the lighting, temperature, and music accordingly. It can also provide real-time information about wait times, elevator capacity, and upcoming floors, which can help reduce passenger anxiety and improve overall satisfaction.In conclusion, computer vision has the potential to revolutionize the way we interact with elevators. From improving safety and security to enhancingaccessibility and user experience, this technology offers a wide array of benefits for both passengers and building owners/operators. With continued research and development, we can expect to see smart elevator systems that are highly efficient, safe, and reliable in the years to comeIn summary, computer vision technology cansignificantly improve the performance andfunctionality of elevators. It can facilitate better crowd management, enhanced security, improved accessibility, and excellent passenger experience. Assurance of these benefits will enable building owners and operators to provide a more efficient, safe, and reliable elevator service. Therefore, we foreseean increase in the adoption of smart elevator systems in the future as this technology continues to advance。
