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Computer vision–based tracking methods are used to track construction resources for productivity and safety purposes. This type of tracking requires that targets be accurately matched across multiple camera views to obtain a three-dimensional (3D) trajectory out of two or more two-dimensional (2D) trajectories. This matching is straightforward when it involves easily distinguishable targets in uncluttered scenes. This can be challenging in industrial scenes such as construction sites due to congestion, occlusions, and workers in greatly similar high-visibility apparel. This paper proposes a novel vision-based method that addresses all these issues. It uses as input the output of a 2D vision-based tracking method and searches for potential matches in three sequential steps. It terminates only when a positive match is found. The first step returns the strongest candidate by correlating a segment of workers’ past 2D trajectories. The second uses geometric restrictions, whereas the third correlates color intensity values. The proposed method features a promising performance of 97% precision, 98% recall, and 95% accuracy.
Konstantinou et al. (Tue,) studied this question.