Silhouette-Based On-Site Human Action Recognition in Single-View Video

On-site worker observation is a fundamental task for a wide spectrum of construction applications such as safety behavior monitoring and productivity analysis. Vision-based action recognition techniques have been proposed to complement the time-consuming and labor-intensive tasks involved in manual observation. In construction, however, previous studies have mainly utilized an RGB-D sensor (e.g., Microsoft Kinect), the operating conditions of which (e.g., active ranges from 80 cm to 4 m, sensitivity to sun light) may hinder the application to actual construction jobsites. To address these issues, we propose a silhouette-based human action recognition method using a single video camera that has less operational constraint. In this framework, the human worker is localized and tracked throughout the monocular video, based on both spatial (i.e., contour of worker) and temporal changes (i.e., moving direction and speed over consecutive frames). Then human action models are learned with temporally adjacent frames and utilized to recognize similar actions in testing video by computing the similarity between the learned action model and newly computed model in a testing dataset. For performance evaluation, we carried out lab experiments, in which a video camera was installed 5-10 m from multiple human subjects. Results indicate that the proposed framework performs well (i.e., an accuracy of 90.68%) to capture predefined poses (e.g., walking, lifting, crawling) in image sequences. This study thus explores an automated means for worker monitoring which potentially helps understand and measure human motions without significant human effort.