GPU Accelerated Non-Parametric Background Subtraction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-11-10

AUTHORS

William Porr , James Easton , Alireza Tavakkoli , Donald Loffredo , Sean Simmons

ABSTRACT

Accurate background subtraction is an essential tool for high level computer vision applications. However, as research continues to increase the accuracy of background subtraction algorithms, computational efficiency has often suffered as a result of increased complexity. Consequentially, many sophisticated algorithms are unable to maintain real-time speeds with increasingly high resolution video inputs. To combat this unfortunate reality, we propose to exploit the inherently parallelizable nature of background subtraction algorithms by making use of NVIDIA’s parallel computing platform known as CUDA. By using the CUDA interface to execute parallel tasks in the Graphics Processing Unit (GPU), we are able to achieve up to a two orders of magnitude speed up over traditional techniques. Moreover, the proposed GPU algorithm achieves over 8x speed over its CPU-based background subtraction implementation proposed in our previous work [1]. More... »

PAGES

629-639

References to SciGraph publications

  • 2015. An Efficient Non-parametric Background Modeling Technique with CUDA Heterogeneous Parallel Architecture in ADVANCES IN VISUAL COMPUTING
  • 2008. Σ-Δ Background Subtraction and the Zipf Law in PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-03801-4_55

    DOI

    http://dx.doi.org/10.1007/978-3-030-03801-4_55

    DIMENSIONS

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