摘要
为了保证特征提取的有效性,更完备地描述人体行为序列,提出了一种基于Zernike矩的人体行为识别方法.该方法利用规范化的运动历史图像(MHI)进行图像序列的表示,从中提取出基于Zernike矩的统计描述作为特征向量进行识别.同时,提出了一种利用图像的重建过程确定分类时采用的Zernike矩的最高阶次的算法.实验中,对8类不同的人体行为进行了测试.应用Zernike矩特征的分类精度高于用规则矩和Hu矩作为特征的方法,证明了基于Zernike矩的人体行为识别方法的有效性.
To ensure the validity and completeness of feature extraction, a new method of recognition of human action using Zernike moments-based features is introduced. In the proposed method, normalized motion history image for motion representation is valued. Statistical descriptions are then computed from motion history image using Zernike moment-based features for the following recognition. A systematic reconstruction- based method for deciding the highest order of Zernike moments required in a classification problem is developed. Experiments are conducted using instances of eight human actions（i, e. eight classes） performed by different subjects. Experiment results show that Zernike moment features for the recognition of human action are superior to regular moments and Hu monents in the accuracy of classification.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2005年第4期423-426,433,共5页
Journal of Beijing University of Technology
基金
北京市自然科学基金资助项目(40031004)北京市教育委员会科技发展基金资助项目(km200310005006).
