Lightweight Random Ferns using binary representation
In many applications which require real-time keypoint recognition such as Augmented Reality, Random Ferns (RF) is widely used due to its runtime performance. It relies on an offline training phase during which runtime computational burdens are delegated. This leads to robust, accurate, and framerate performance. However, it requires significant amounts of memory, and this has been an obstacle to its use in industry, especially in mobile environments. In this paper, we propose Lightweight Random Ferns to reduce the memory requirements of RF by modifying the representation of probabilities used in ferns to a single bit from floating point. As a result, the total memory requirements of RF are significantly reduced.