| Less contact and less gathering have become new requirements for people’s production and lives in light of the global pandemic of the new crown epidemic(COVID-19).Many enterprises and institutions have begun to implement a policy of employees working from home in order to respond to the state’s call and maintain social security and stability,which is especially evident for customer service personnel in the tertiary sector.Low work efficiency and regulatory issues,on the other hand,have always been the biggest drawbacks of the home office model.Currently,"weak surveillance " methods such as punching in and out of work and writing daily and weekly reports are used to supervise customer service workers who work from home.This strategy relies heavily on managers’ experience,is time-consuming,and has little impact.This paper uses a "strong surveillance " system that can meet real-time video monitoring to make it easier to supervise home customer service personnel and improve the efficiency of the home office.The images captured by the camera are sent to the supervision terminal for real-time playback,and this surveillance system is widely used in the field of kitchen systems and security monitoring.However,due to the unique nature of the customer service home office environment,direct monitoring will inevitably result in privacy and security concerns.In order to solve the privacy and security problems in the "strong surveillance " system,this paper relies on the development of artificial intelligence-related technologies to add background replacement functions to the surveillance system to avoid direct exposure of the customer service home environment to the system.This paper begins with an in-depth examination of audio and video coding technology,as well as the streaming media transmission protocol,before concluding with the technical selection of a video customer service surveillance system.Then,for the one-of-a-kind scenario of customer service representatives working from home,a dataset was chosen and built specifically for this scenario.The SE-ICNet algorithm model is built in this paper by introducing the SE module,which is a channel attention mechanism.Simultaneously,in order to ensure the algorithm model’s stable operation in the event of limited resources,the algorithm model is pruned and compressed to reduce the model’s complexity and,finally,to make it intelligible.It has a pixel accuracy of more than 90%on the construction data set,which is based on the premise of ensuring processing speed.The efficiency of this model is then confirmed through a series of comparative and ablation experiments.This paper designs and implements a video customer service surveillance system that supports real-time background replacement based on the previous work.The system is divided into seven modules in this paper,which include a login module,a monitoring module,an algorithm module,a data persistence module,a business processing module,a visualization module,and a streaming media server.The algorithm model presented in this paper is used by the algorithm module.Finally,the system’s ability to meet scene usage requirements and design expectations is verified through a series of comprehensive system tests.To summarize,the goal of this paper is to increase image segmentation speed while maintaining a high level of segmentation accuracy,reduce the device’s hardware requirements,and make it run stable and smoothly on a user’s low-end computer.A stable,low-latency customer service surveillance system with real-time background replacement is designed and implemented as a result of this.The groundbreaking paves the way for a practical solution to the regulatory issue of customer service representatives working from home. |
