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 nonrigid 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.
Air filters, Artificial intelligence, Color, Computer vision, feature extraction, image processing, Nonlinear filtering, Optical properties, Pattern recognition, Signal filtering and prediction, Standards, Wave filters
Date of this Version
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops (2008)
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