摘要
人体的运动过程较为复杂,图像中的相似动作很多,对传统的特征识别形成干扰,造成识别准确性不高。为了提高其识别正确率,提出一种Hu不变矩和人工鱼群优化支持向量机的人体运动姿态识别模型(Hu-AFSA-SVM)。首先,以二维连续图像为基础,提取图像中人体运动姿态识别的7个Hu不变矩,然后将其输入到SVM中进行训练,并采用AFSA对SVM参数进行优化,通过寻找一个最优超平面,尽可能在满足分类的限制条件下,将所有人体运动姿态分类数据集中的类别分开,在克服干扰下,完成识别。最后对其进行仿真实验。仿真结果表明,相对于其它识别模型,Hu-AFSA-SVM提高了人体运动姿态识别正确率,同时加快了识别速度,是一种有效的人体运动姿态识别方法。
The body＇s movement process is relatively complex,so there are many similar movements in the images,forming interferences on the characteristics of the traditional recognition,and causing low recognition accuracy.In order to improve its recognition accuracy,this paper put forward a kind of Hu moment invariants and artificial fish optimization support vector machine （SVM） model for human motion recognition （Hu-AFSA-SVM）.First of all,based on the twodimensional continuous images,extracted the image 7 Hu moment invariants of the human body movement gesture recognition,and then input into SVM to train,and picked a AFSA to SVM parameter optimization,to find an optimal hyperplane,in as much as possible meet the constraints of classification,all concentrated human motion data classification categories,complete recognition.Finally carried out the simulation experiment.Simulation results show that,compared with other identification model,Hu-AFSA-SVM improves the human motion recognition accuracy,speeds up the recognition,and is an effective method for human movement gesture recognition.
出处
《计算机科学》
CSCD
北大核心
2014年第3期306-309,共4页
Computer Science
基金
国家自然科学基金(61202285)资助
关键词
人体运动姿态
支持向量机
不变矩特征
人工鱼群算法
识别
Human gesture
Support vector machine
Moment invariants
Artificial fish swarm algorithm
Recognition
