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

Abstract

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.