Scalable hardware architecture for disparity map computation and object location in real-time

Abstract

We present the disparity map computation core of a hardware system for isolating foreground objects in stereoscopic video streams. The operation is based on the computation of dense disparity maps using block-matching algorithms and two well-known metrics: sum of absolute differences and Census transform. Two sets of disparity maps are computed by taking each of the images as reference so that a consistency check can be performed to identify occluded pixels and eliminate spurious foreground pixels. Taking advantage of parallelism, the proposed architecture is highly scalable and provides numerous degrees of adjustment to different application needs, performance levels and resource usage. A version of the system for 640 × 480 images and a maximum disparity of 135 pixels was implemented in a system based on a Xilinx Virtex II-Pro FPGA and two cameras with a frame rate of 25 fps (less than the maximum supported frame rate of 40 fps on this platform). Implementation of the same system on a Virtex-5 FPGA is estimated to achieve 80 fps, while a version with increased parallelism is estimated to run at 140 fps (which corresponds to the calculation of more than 5.9 × 109 disparity-pixels per second).