Action recognition of construction workers under occlusion

The capability to recognize the actions of workers through videos with adequate accuracy is of great significance in construction engineering. Most of the current action recognition models, however, tend to loss their effectiveness in case of occlusion problem. In order to address this issue, a skeleton-based action recognition method is proposed in this paper. The essence of the proposed approach is to transform the occlusion problem into the process of imputing missing values of the feature matrix. The method can be implemented by following three steps. Firstly, a new encoding technique is developed to transform the recognized worker skeleton into a feature matrix. Followed by integrating an attention model into the GAN (Generative Adversarial Networks)-based data imputation model, the missing data can be effectively focused on. Finally, the Resnet (Residual Network) classification model will be introduced to determine the category of actions through the classification of imputed feature matrix. To verify the accuracy of the proposed method, a dataset containing eight actions is established based on which the method will be tested. Analytical results indicate that the method can improve the reorganization accuracy to 93.89% when there is no occlusion, and the reorganization accuracy to 86.62% when there is occlusion. At the same time, the proposed method has limitations, it will show the greatest advantage with a moderate occlusion rate.