Stream-Based ORB Feature Extractor with Dynamic Power Optimization

The Oriented Fast and Rotated BRIEF (ORB) feature extractor, which consists of key-point detection and descriptor computation, is a key module in many computer vision systems. Existing hardware implementations of ORB feature extractor only focus on increasing performance with power optimization as a post consideration. In this paper, we present a stream-based ORB feature extractor that incorporates mechanisms to lower the dynamic power consumption. These mechanisms exploit the fact that the number of detected keypoints is typically small. The proposed solution significantly lowers the switching activity of the key-point detection and descriptor computation stages by early pruning of non-likely key-points and gating the descriptor computation stages. Further power reduction and resource minimization are achieved by employing a threshold-guided bit-width optimization strategy to truncate the redundant bits in the key-point detection stage. Finally, we propose an approximation method to achieve rotation invariance of the descriptors. FPGA implementation targeting the Altera Aria V device shows that the proposed strategies lead to over 25% reduction in dynamic power and lower resource utilization, with only marginal loss in accuracy.

I. Introduction

Visual feature extraction is a fundamental module in many computer vision systems found in robotics, augmented reality, and autonomous vehicles. Many feature descriptors such as Scale-Invariant Feature Transform (SIFT) [1], Speeded Up Robust Features (SURF) [2], Oriented Fast and Rotated BRIEF (ORB) [3], etc. have been previously explored. Studies in [4]–[7] have shown that the ORB descriptor achieves better performance compared to other descriptors.