Face Tracking via Block Texture Feature Based Mean Shift
Zhao, Chunshui; Liu, Zhiyong; Qiao, Hong
2008
会议名称4th International Conference on Natural Computation (ICNC 2008)
会议录名称FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION
会议日期OCT 18-20, 2008
会议地点Jian, PEOPLES R CHINA
摘要Face tracking plays an important role in many computer vision applications such as human-robot interaction and visual surveillance. However, it is still a challenging problem, due to various factors related to illumination cluttered background and poses variations. In this paper, we introduce a novel feature descriptor, namely Block Binary Pattern (BBP), to represent the appearance model of face for the tracking tasks. Compared to Local Binary Pattern (LBP), BBP has the advantage of capturing multi-scale structure, while preserving the robustness to illumination and appearance variations, and meantime, it can be extracted in real-time for real-world applications. Based on the BBP features, we use AdaBoost strategy to select a discriminative features pool. These features can be considered as the prior appearance model of face. We use similarity-based mean-shift, which is the extension of original mean-shift, as the face tracker. Experimental results on challenging sequences validate the effectiveness of our method for face tracking.
关键词Visual Tracking
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12812
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Zhao, Chunshui
作者单位Chinese Acad Sci, Inst Automat
推荐引用方式
GB/T 7714
Zhao, Chunshui,Liu, Zhiyong,Qiao, Hong. Face Tracking via Block Texture Feature Based Mean Shift[C],2008.
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