GPU-Accelerated Gaussian Processes for Object Detection

Calum Blair, John Thompson, Neil Robertson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Gaussian Process classification (GPC) allows accurate and reliable detection of objects. The high computational load of squared-error or radial basis function kernels limits the applications that GPC can be used in, as memory requirements and computation time are both limiting factors. We describe our version of accelerated GPC on GPU (Graphics Processing Unit). GPUs have limited memory so any GPC implementation must be memory-efficient as well as computationally efficient. Using a high-performance pedestrian detector as a starting point, we use its packed or block-based feature descriptor and demonstrate a fast matrix multiplication implementation of GPC which is also extremely memory efficient. We demonstrate a speed up of 3.7 times over a multicore, BLAS-optimised CPU implementation. Results show that this is more accurate and reliable than results obtained from a comparable support vector machine algorithm.

Original languageEnglish
Title of host publication2015 Sensor Signal Processing for Defence (SSPD)
PublisherIEEE
ISBN (Print)9781479974443
DOIs
Publication statusPublished - 2015
Event5th Sensor Signal Processing for Defence 2015 - Edinburgh, United Kingdom
Duration: 9 Sep 201510 Sep 2015

Conference

Conference5th Sensor Signal Processing for Defence 2015
Abbreviated titleSSPD 2015
CountryUnited Kingdom
CityEdinburgh
Period9/09/1510/09/15

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation

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  • Cite this

    Blair, C., Thompson, J., & Robertson, N. (2015). GPU-Accelerated Gaussian Processes for Object Detection. In 2015 Sensor Signal Processing for Defence (SSPD) [7288505] IEEE. https://doi.org/10.1109/SSPD.2015.7288505