A survey on parallel computing for traditional computer vision. (28th September 2021)

• Record Type: Journal Article
• Authors: Jaiswal, Deepak; Kumar, Praveen
• Is Part Of: Concurrency and computation. Volume 34:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-28
• Subjects: discrete GPU -- edge computing -- integrated GPU -- review -- traditional computer vision; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6638
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The applications of computer vision (CV) are continuously increasing along with the enormous demand for real‐time data processing. This visual data processing is done with various compute‐intensive image/video processing algorithms that may belong to traditional approaches or deep learning approaches. This article aims to provide a survey of state‐of‐the‐art hardware platforms and software frameworks for parallel implementation of traditional CV applications. The article discusses various options for hardware platforms for centralized‐computing architecture and edge‐computing architecture, and various software frameworks that can be used to leverage the hardware. This discussion is based on a systematic survey of studies/works that show the use of various hardware platforms and software frameworks in order to achieve real‐time processing for CV algorithms. Based on the survey, some possible future directions are also discussed.