Adaptive Sampling for Feature Detection, Tracking, and Recognition on Mobile Platforms

Local image features have become ubiquitous for a wide range of computer vision tasks. For embedded and low power devices, speed and memory efficiency is of main concern, and therefore, there have been several recent attempts to improve these issues. In this paper, we are concerned with the early components of the object recognition pipeline, namely, feature detection, feature description, and feature tracking. In particular, we propose a novel approach to speed up feature detectors and to inform feature tracking that speeds up the recognition process by using the concept of adaptive sampling. We select two examples of visual algorithms to be modified by adaptive sampling and present comparative results with and without modifications. We show how processing time and memory footprint can benefit by this approach with little impact on overall output quality. We implement our proposed methods on a chipset commonly found on smartphones and we discuss the obtained improvements.