Design And Implementation Of Worker Construction State Recognition System Based On Posture Estimation Algorithm

The development mode of the construction industry is relatively extensive,the skills and quality of construction workers are not high,and the supervision system is backward,making it difficult to count the construction status of workers.At the construction site,identifying the workers’ construction status and obtaining the workers’ construction efficiency can enable managers to accurately quantify and benchmark labor productivity,thereby ensuring the progress of the construction.The current evaluation of workers’ productivity on construction sites relies entirely on manual observation.It takes a lot of time and manpower to obtain workers’ construction status data on complex construction sites,and it is impossible to calculate the construction status of each worker in real time.In response to the above problems,researchers now use motion recognition algorithms to identify workers’ activities and analyze construction efficiency.However,at present,only single workers can be analyzed.When the number of construction workers increases,accurate analysis cannot be performed and it cannot meet the actual demand.In this paper,based on the improved pose estimation algorithm to obtain the keypoint of the human body,the study is based on the data of the keypoint of the human body to track and identify the movement of the workers to obtain the construction efficiency of the workers.A keypoint dataset of construction actions was produced to train the action recognition network,a recognition system was built,and finally the overall test was conducted.The main work of the thesis is as follows:(1)This article uses human body keypoint data for motion analysis,considering that the actual construction site needs to process multi-channel camera video information at the same time.Lightweight improvements were made to the problem of large computational volume and slow processing speed of the pose estimation network Open Pose for extracting keypoint data of the human body.Replace the VGG-19 backbone network with Mobile Net V2,and the separable convolution replaces the ordinary convolution in the two branch networks.The improved network will be tested on the COCO data set.The accuracy is lower than the original,but it is extracted in the actual video test.To the keypoint information,the result is not big,and the actual video processing speed is improved.(2)Considering the problem of camera shake and worker occlusion on the construction site,we completed the design of the keypoint tracking algorithm,and chose to use the multi-person tracking algorithm Deep Sort,using the keypoint information to extract the worker’s bounding box,complete the extraction of motion information and appearance information,and effectively track the workers and keypoint Information.The test results show that the algorithm is suitable for the application on site.(3)Collect workers’ construction actions from multiple angles,establish keypoint action data sets,and extract center of gravity features,angle features,and speed features with time information from the datasets.Considering the time continuity of actions,time series analysis is performed on keypoint,and a multi-layer MLP network and a stacked LSTM network are used to train keypoint datasets.The test results show that the adoption of datasets with time information and stacked LSTM networks improves the accuracy of action recognition.At the same time,the system has been tested overall,and the running speed can basically meet the actual needs.