Accelerating Stereo Matching on Mutlicore ARM Platform

Abstract

Stereo vision is a well-known technique in computer vision used to acquire the 3D depth information of a scene from two or more 2D images. One of the main issues with any stereo vision system is how to make a good trade off between the processing speed and the quality of the disparity map. This issue can be resolved through the use of dedicated hardware platforms, like Field Programmable Gate Arrays and Graphical Processing Units, which are considered as expensive solutions. In this work, the challenge of accelerating stereo matching on low cost multicore platforms is tackled. We present a novel software implementation of a sparse Rank algorithm, that uses a modified Sum of Absolute Differences 1D box filtering algorithm in the correlation stage. Consequently, we reduce the number of computations and memory space needed for computing the disparity map. The system is implemented on a multicore Advanced Risc Machine platform (ODROID XU4). Experimental results show that the system is capable of achieveing a processing speed of 59 Frames Per Second for images of size 320×240 pixels with a disparity range of 20 pixels. Furthermore, the sparse Rank structure does not affect significantly the overall quality of the disparity map.