Document Type

Article

Language

eng

Format of Original

9 p.

Publication Date

11-2010

Publisher

Elsevier

Source Publication

Computer Vision and Image Understanding

Source ISSN

1077-3142

Original Item ID

doi: 10.1016/j.cviu.2010.03.020

Abstract

We present a parallel implementation of a histogram-based particle filter for object tracking on smart cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights and parallel construction of the feature histograms since these are the major bottlenecks in standard implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-based feature sets—we show in detail how the parallel particle filter can employ simple color histograms as well as more complex histograms of oriented gradients (HOG). The algorithm was successfully implemented on an SIMD processor and performs robust object tracking at up to 30 frames per second—a performance difficult to achieve even on a modern desktop computer.

Comments

Accepted version. NOTICE: this is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, Vol. 114, No. 11 (November 2010): 1264-1272. DOI. © 2010 Elsevier. Used with permission.

Henry Medeiros was affiliated with Purdue University at the time of publication.

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