A Comparative Study of Grayscale Conversion Techniques Applied to Descriptor Based Tracking

In computer vision, gradient-based tracking is usually performed from monochromatic inputs. However, few researches consider the influence of the chosen color-to-grayscale conversion technique. This paper evaluates the impact of these conversion algorithms on tracking and homography calculation results, both being fundamental steps of augmented reality applications. Eighteen color-to-greyscale algorithms were investigated. These observations allowed to conclude that the methods can cause significant discrepancy in the overall performance. As a related finding, experiments also showed that pure color channels (R, G, B) yielded more stability and precision when compared to other approaches.