A Parallel Color-Based Particle Filter for Object Tracking

Henry Medeiros, Marquette University
Johnny Park, Purdue University
Avinash Kak, Purdue University

Published as part of the proceedings of the conference, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008: 1-8. DOI: 10.1109/CVPRW.2008.4563148.

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


Porting well known computer vision algorithms to low power, high performance computing devices such as SIMD linear processor arrays can be a challenging task. One especially useful such algorithm is the color-based particle filter, which has been applied successfully by many research groups to the problem of tracking non-rigid objects. In this paper, we propose an implementation of the color-based particle filter suitable for SIMD processors. The main focus of our work is on the parallel computation of the particle weights. This step is the major bottleneck of standard implementations of the color-based particle filter since it requires the knowledge of the histograms of the regions surrounding each hypothesized target position. We expect this approach to perform faster in an SIMD processor than an implementation in a standard desktop computer even running at much lower clock speeds.