A new parallel particle filter face tracking method based on heterogeneous system

This paper proposed a multi-cue-based face-tracking algorithm with the supporting framework using parallel multi-core and one Graphic Processing Unit (GPU). Due to illumination and partial-occlusion problems, face tracking usually cannot stably work based on a single cue. Focusing on the above-menti...

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Bibliographic Details
Published in:Journal of real-time image processing Vol. 7; no. 3; pp. 153 - 163
Main Authors: Liu, Ke-Yan, Li, Yun-Hua, Li, Shanqing, Tang, Liang, Wang, Lei
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.09.2012
Springer Nature B.V
Subjects:
Adaptive algorithms
Algorithms
Computer Graphics
Computer Science
Cues
Electromagnetic wave filters
Graphics processing units
Histograms
Hypotheses
Illumination
Image Processing and Computer Vision
Methods
Microprocessors
Multimedia Information Systems
Occlusion
Pattern Recognition
Signal,Image and Speech Processing
Special Issue
Tracking
Face tracking
Multi-core
General purpose computing on Graphic Processing Unit
Particle filter
ISSN:1861-8200, 1861-8219
Online Access:Get full text
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Summary:This paper proposed a multi-cue-based face-tracking algorithm with the supporting framework using parallel multi-core and one Graphic Processing Unit (GPU). Due to illumination and partial-occlusion problems, face tracking usually cannot stably work based on a single cue. Focusing on the above-mentioned problems, we first combined three different visual cues—color histogram, edge orientation histogram, and wavelet feature—under the framework of particle filters to considerably improve tracking performance. Furthermore, an online updating strategy made the algorithm adaptive to illumination changes and slight face rotations. Subsequently, attempting two parallel approaches resulted in real-time responses. However, the computational efficiency decreased considerably with the increase of particles and visual cues. In order to handle the large amount of computation costs resulting from the introduced multi-cue strategy, we explored two parallel computing techniques to speed up the tracking process, especially the most computation-intensive observational steps. One is a multi-core-based parallel algorithm with a MapReduce thread model, and the other is a GPU-based speedup approach. The GPU-based technique uses features-matching and particle weight computations, which have been put into the GPU kernel. The results demonstrate that the proposed face-tracking algorithm can work robustly with cluttered backgrounds and differing illuminations; the multi-core parallel scheme can increase the speed by 2–6 times compared with that of the corresponding sequential algorithms. Furthermore, a GPU parallel scheme and co-processing scheme can achieve a greater increase in speed (8×–12×) compared with the corresponding sequential algorithms.
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-011-0225-6