Design of High-Performance HOG Feature Calculation Circuit for Real-Time Pedestrian Detection

Abstract

This paper proposes the design of high-performance histogram of oriented gradient (HOG) feature calculation circuit for real-time pedestrian detection. By utilizing thoroughly analyzed results of the operations for overlapping blocks and windows and by managing internal memories and registers to store the intermediate results of HOG feature efficiently, not only all redundant operations are totally removed but also trilinear interpolation technique is successfully applied in the proposed circuit. The proposed circuit can process variable sizes of input image up to full high-definition (HD) image and it supports two types of detection window and color format of input image. In order to accelerate the processing time, the proposed circuit adopts the parallel architecture with pipelines, and the external memory bandwidth is minimized by the efficient management of internal memories and registers. The circuit size is reduced by sharing the circuit resources for the common operations and by minimizing the required storage spaces. Even though a large amount of computations is required due to trilinear interpolation, the proposed circuit can process full HD images in real time, assuming a scaling factor of 0.9. Therefore, it can be used for real-time pedestrian detection in many applications.