|Real-time Safety Monitoring Vision System for Linemen in Buckets Using Spatio-temporal Inference
Zahid Ali and Unsang Park*
International Journal of Control, Automation, and Systems, vol. 19, no. 1, pp.505-520, 2021
Abstract : Linemen risk falls, electric shocks, burns, and other injuries during the daily job and these incidents can often be fatal. In this paper, we present a novel vision-based real-time system for detection and tracking of various non-rigid safety wearables worn by linemen, in a highly cluttered environment. We set up four imaging sensors on the repair truck’s bucket to robustly monitor the linemen from four different viewpoints. In the monitoring system, we firstly apply a novel fast background segmentation method to suppress false positives and reduce search space. Next, we represent each safety wearable with a Gaussian mixture model and track them with an LK-tracker. In order to track occluded or out-of-camera-view safety wearables, we propose a novel human pose inference method. The proposed method is an extension from the existing CNN-based human pose inference by utilizing light-weight color, shape, and space-based human pose inference mechanism. The proposed human pose inference method shows
improved performance in terms of precision, recall, and speed. Experimental results on a number of challenging sequences demonstrate the effectiveness of the proposed scheme, under complex background, prolonged occlusions, and varying color, shape, and lighting.
Gaussian mixture model, linemen safety monitoring, object detection, pose inference.
Download PDF : Click this link