A vision-based motion capture and recognition framework for behavior-based safety management

In construction, about 80%–90% of accidents are associated with workersʹ unsafe acts. Nevertheless, the measurement of workersʹ behavior has not been actively applied in practice, due to the difficulties in observing workers on jobsites. In an effort to provide a robust and automated means for worker observation, this paper proposes a framework of vision-based unsafe action detection for behavior monitoring. The framework consists of (1) the identification of critical unsafe behavior, (2) the collection of relevant motion templates and site videos, (3) the 3D skeleton extraction from the videos, and (4) the detection of unsafe actions using the motion templates and skeleton models. For a proof of concept, experimental studies areundertaken to detect unsafe actions during ladder climbing (i.e., reaching far to a side) in motion datasets extracted from videos. The result indicates that the proposed framework can potentially perform well at detecting predefined unsafe actions in videos.