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基于粒子滤波多视角变尺度视频人脸的实时跟踪

Multi-view and Multi-scale Face Tracking Based on Particle-filter
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摘要 提出了一种新的可在复杂环境下,人脸方向、姿态、尺度都有很大变化时,仍能够稳健跟踪的视频跟踪人脸的算法框架。该算法框架很好地结合了人脸检测器和粒子滤波的优点,利用人脸基本模型和特定模型,建立多视角人脸跟踪器,并使用实时跟踪结果,不断对多视角人脸模型进行在线学习,很好地克服了在传统视频跟踪过程中,由于人脸的旋转、尺度变化所造成的漂移甚至失败。实验结果表明,该算法对人脸的旋转、尺度变化、环境影响不敏感,具有很强的鲁棒性和精确性。 This paper proposes a novel algorithm framework designed for robust face tracking under complex environment.It can address large changes in the direction,pose and scale of the tacking face.The framework combines the advantages of face detection and particle-filter,and the online learning using tracking results ensures the stability of tracking.Experiments demonstrate that algorithm can handle multi-view and multi-scale face tracking steadily.
作者 马波 周越
出处 《微型电脑应用》 2010年第11期36-38,5,共3页 Microcomputer Applications
基金 国家863项目(2007AA01Z164) 国家自然科学基金项目(60772097)
关键词 REAL ADABOOST 粒子滤波 在线学习 多视角跟踪 变尺度跟踪 贝叶斯网络 Real AdaBoost Particle-filter Online Learning Multi-view Tracking Multi-scale Tracking Bayesian Network
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参考文献7

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