SOFF: Scalable and oriented FAST-based local features

Abstract

Local feature detection is a fundamental module in several mobile vision applications such as mobile object recognition and mobile visual search. The effectiveness and the efficiency of a local feature detector decide to what extent it is suitable for a mobile application. Over the past decades, several local feature detectors have been developed. In this paper, we are interested in FAST (Features from Accelerated Segment Test) local feature detector for its efficiency. However, FAST detector shows poor robustness against both scale and rotation changes. Therefore, we aim at enhancing FAST robustness against both scale and rotation changes while maintaining good efficiency. To this end, we propose a Scalable and Oriented FAST-based local Feature detector (SOFF). A comprehensive comparison against FAST detector and its variants is performed on benchmark datasets. Experimental results demonstrate that SOFF detector outperforms other FAST-based detectors in many cases. Furthermore, it is efficient to compute, thereby suitable for mobile vision applications.