| 摘要: |
| 提出一种对视频流中的连续手势进行检测和分类的方法.检测的目的是找到这些手势的开始帧和结束帧.提出的融合音频和视觉信息的检测方法确保了检测结果的鲁棒性和正确率.对于检测到的手势，提出一种通过在Grassmann流形下精确度量其协方差矩阵距离的分类方法以有效区分不同类的手势.方法在ChaLearn Multimodal Gesture dataset 2013上进行测试，取得了很高的识别率，Recall和Precision均达到93%以上. |
| 关键词: 手势检测与分类 协方差矩阵 Grassmann流形 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金（61472398)；中国航天医学工程预先研究项目（2013SY54A1303) |
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| Dynamic Gesture Detection and Classification |
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WANG Han-Jie, CHAI Xiu-Juan, CHEN Xi-Lin
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Key Laboratory of Intelligent Information Processing of the Chinese Academy of Sciences(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China
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| Abstract: |
| This paper proposes a framework of gesture detection and classification in continuous sequence data. The goal of detection is to determine the start and end frame of a gesture in the continuous sequence. The detection method using multi-modal features ensures the robustness and high accuracy. To classify the detected gestures represented by covariance matrices, a distance measurement on Grassmann manifold is presented to strengthen the discriminative power. The framework is evaluated on ChaLearn Multimodal Gesture dataset 2013 and achieves high accuracy. Both Recall and Precision are higher than 93%. |
| Key words: gesture detection and classification covariance matrices Grassmann manifold |
