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Face tracking based on differential harmony search

机译：基于差分和声搜索的人脸跟踪

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摘要

Owing to its significant roles in computer vision applications, human face tracking has drawn extensive attention in recent years. Most researchers solve face tracking using particle filter, meanshift and their derivatives. Unlike the traditional methods, in this study, face tracking is treated as an optimisation problem and a new meta-heuristic optimisation algorithm, differential harmony search (DHS), is introduced to solve face tracking problems. We compare the speed and accuracy of the proposed method with particle filter, meanshift and improved harmony search. Experimental results show that DHS-based tracker is faster and more accurate and it is easy to handle the parameters tuning. Furthermore, to improve the reliability of tracking, multiple visual cues are applied to DHS-based tracking system and experimental results demonstrate the increased robustness achieved by fusing multiple cues.
机译：由于其在计算机视觉应用中的重要作用，近年来人脸跟踪已引起广泛关注。大多数研究人员使用粒子滤波，均值漂移及其导数来解决人脸跟踪问题。与传统方法不同，本研究将人脸跟踪视为一个优化问题，并引入了一种新的元启发式优化算法差分和声搜索（DHS）来解决人脸跟踪问题。我们将所提方法的速度和准确性与粒子滤波，均值漂移和改进的和声搜索进行了比较。实验结果表明，基于DHS的跟踪器更快，更准确，并且易于处理参数调整。此外，为了提高跟踪的可靠性，将多个视觉提示应用于基于DHS的跟踪系统，实验结果表明，通过融合多个提示可以提高鲁棒性。

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