Vision-based Fall Detection in Aircraft Maintenance Environment with Pose Estimation

Fall-related injuries at the workplace account for a fair percentage of the global accident at work claims according to Health and Safety Executive (HSE). With a significant percentage of these being fatal, industrial and maintenance workshops have great potential for injuries that can be associated with slips, trips, and other types of falls, owing to their characteristic fast-paced workspaces. Typically, the short turnaround time expected for aircraft undergoing maintenance increases the risk of workers falling, and thus makes a good case for the study of more contemporary methods for the detection of work-related falls in the aircraft maintenance environment. Advanced development in human pose estimation using computer vision technology has made it possible to automate real-time detection and classification of human actions by analyzing body part motion and position relative to time. This paper attempts to combine the analysis of body silhouette bounding box with body joint position estimation to detect and categorize in real-time, human motion captured in continuous video feeds into a fall or a non-fall event. We proposed a standard wide-angle camera, installed at a diagonal ceiling position in an aircraft hangar for our visual data input, and a three-dimensional convolutional neural network with Long Short-Term Memory (LSTM) layers using a technique we referred to as Region Key point (Reg-Key) repartitioning for visual pose estimation and fall detection.