A performance evaluation of vision and radio frequency tracking methods for interacting workforce

Analysis and understanding of worksite operations requires the collection of trajectory information regarding active onsite entities. The trajectories provide essential information for assessment of site operations and management strategies. Unfortunately, many such tracking and assessment methods are based on manually generated trajectories, thereby excluding long term and complex scenarios. Automated trajectory extraction is a potential solution, however the automated method must provide reliable trajectory information. This paper evaluates the capability of two sensor technologies to provide accurate trajectory data. The primary system investigated is a vision-based strategy while the secondary system investigated is an Ultra Wideband System (UWB). A robotic total station (RTS) provides the ground truth of a single worker for evaluation of both sensor technologies. For comparison, the visual tracking signal is mapped to three-dimensional world coordinates. The UWB and RTS signals are filtered to remove outliers, and are synchronized with the visual tracking signal. Given the single target tracked by the RTS, both UWB and vision are shown to be feasible when the expected accuracy is 1m. A second investigation seeks to demonstrate the accuracy of the proposed visual tracking system for tracking multiple interacting workforce, which relies on machine learning to generate target descriptions. The UWB signals for the personnel provide ground truth to evaluate the reliability and accuracy of the vision-based tracking algorithm. The proposed visual tracking system is reliable and accurate enough to permit automated extraction of trajectory information for analysis purposes, such as would be required for work sampling and productivity assessments.