ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Towards Improved Observation Models for Visual Tracking: Selective Adaptation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Web-scale computer vision using MapReduce for multimedia data mining
Proceedings of the Tenth International Workshop on Multimedia Data Mining
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This paper proposed a multi-cue based face tracking algorithm with the help of parallel multi-core processing. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of particle filter to improve the tracking performance considerably. To handle the huge amount of computation cost resulted from the introduced multicue strategy, a map-reduce thread model is designed to parallel and speed up the observation steps. Besides, an online updating strategy makes our algorithm adaptable to some slight face rotations. The experimental results demonstrate that our proposed face tracking algorithm works robustly for cluttered backgrounds and different illuminations. The multi-core parallel scheme achieves a good linear speedup compared to the corresponding sequential algorithms.