Stereo vision IP design for FPGA implementation of obstacle detection system

Bendaoudi, Hamza; Khouas, Abdelhakim

doi:10.1109/wosspa.2013.6602352

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Table 3 contains a comparison of the accuracy of the proposed system with other related works on different platforms. The comparison is performed for the above four images, using the statistical error metric defined in Equation (10) for three different regions: non-occluded (nonocc), all, and discontinuous (disc). where B r is the percentage of bad pixels in a specific region (r), N r is the total number of pixels in one of the nonocc, all, and disc regions.…”

Section: Resultsmentioning

confidence: 99%

Implementation of a real‐time stereo vision algorithm on a cost‐effective heterogeneous multicore platform

Saidi

Khouas

Amira

2022

Concurrency and Computation

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Summary Stereo vision is a major computer vision technique commonly used for robotics applications. Existing software implementations of this technique on general‐purpose processors offer low time‐to‐market compared to other platforms. However, such implementations can hardly achieve real‐time and their cost is usually relatively high. These issues can be solved by embedded multicore platforms. In this article, we present a low‐cost, improved software implementation of a stereo matching algorithm in the correlation stage that combines a sparse rank transform with a combination of sum of absolute differences 1‐D and 2‐D box filtering algorithms. A circular buffer scheme is used to optimize memory usage during the rank computation stage. The system runs on a heterogeneous multicore platform (ODROID XU4). Through the extensive use of single instruction multiple data Neon intrinsics, the system can process images with a size of 320prefix×240$$ 320\times 240 $$ pixels and a disparity range of 20 pixels at a rate of 111 frames per second. The proposed system can be used in mobile robot platforms that require low power consumption while delivering real‐time performance.

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Section: Resultsmentioning

confidence: 99%