摘要
针对人体行为识别领域中视频序列的大样本及多特征问题,提出一种基于张量的核Fisher非线性鉴别(KFLD)-尺度不变特征变换(SIFT)与相关向量机(RVM)模糊积分融合的人体行为识别方法.该方法首先通过预处理视频序列得到二值视频,并采用三阶张量表示.然后针对大样本特征提出KFLD-SIFT局部特征提取算法,对不同初始尺度下的关键点周围的多特征降维,同时提出RVM模糊积分融合算法进行行为分类.最后应用4种经典评价指标及计算得到的平均识别率对比分析文中方法与其他相关方法的识别效果,数据采用KTH人体行为数据库中的视频,并采用三重交叉方法验证和测试.实验表明文中方法对多种行为取得较好的识别效果,平均识别率比其他主流方法至少提高2.3%.
Due to the large sample and multiple characteristics of video sequence in the field of human action recognition, a method of kernel Fisher nonlinear discriminant （ KFLD） -scale invariant feature transform （ SIFT ） and relevance vector machine （ RVM ） fuzzy integral fusion recognition based on tensor is proposed. Firstly, video sequence is pre-processed into binary video sequence, and then it is described as third-order tensor. Furthermore, as for large sample characteristics, a local feature extraction method of KFLD-SIFT is proposed to reduce the dimension around the key points under different initial scales. Meanwhile, RVM fuzzy integral fusion algorithm for behavior classification is presented. Finally, the proposed method and other relevant methods are compared through four kinds of evolution indexes and average recognition rates. The video sequence of KTH human action database and triple-cross verification method are used to test the recognition methods. Experimental results show that the proposed method achieves good recognition effect, and its average recognition rate rises by at least 2. 3% compared to other mainstream methods for human action recognition.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第8期713-719,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.51205185
61308066)
江苏省博士后科研资助计划项目(No.1001027B)资助
