光学精密工程2017，Vol.25Issue(6)：1669-1678,10.DOI:10.3788/OPE.20172506.1669
采用自适应变异粒子群优化SVM的行为识别
Action recognition based on adaptive mutation particle swarm optimization for SVM
摘要
Abstract
The action recognition framework based on local features was established to improve the recognition ability of human behavior in video sequences.The algorithms related to the framework were researched through spatial temporal features extracting and encoding and parameters optimization of SVM classifier.Firstly,the feature descriptors composed of Histograms of Oriented Gradients(HOG)and Histograms of Optical Flow(HOF)were used to describe Space Time Interest Points(STIP)achieved by the Harris 3D detector and then encoded by Fisher Vector(FV).Due to the generalization ability of Support Vector Machine(SVM)model for action classification under fixed parameters was insufficient,the particle swarm optimization algorithm was applied to the parameter optimization of each action classifier.According to the characteristics of population diversity changed from generation to generation,the constructed particles aggregation degree model was used to adjust mutation probability for each generation of particles dynamically.Finally,the proposed method was verified by KTH and HMDB51 data sets.The results show that the Adaptive Mutation Particle Swarm Optimization(AMPSO)algorithm can avoid the local optimum and has strong global optimization capability.The recognition accuracies on KTH and HMDB51 data sets are 87.50% and 26.41%,respectively,which are better than two recognition methods.Experimental results indicate that the AMPSO algorithm has good convergence performance and the overall recognition framework has high practicability and accuracy.关键词
人体行为识别/自适应变异粒子群算法/时空兴趣点/特征编码/支持向量机Key words
human action recognition/Adaptive Mutation Particle Swarm Optimization(AMPSO)/Space Time Interest Points(STIP)/feature coding/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
张国梁,贾松敏,张祥银,徐涛..采用自适应变异粒子群优化SVM的行为识别[J].光学精密工程,2017,25(6):1669-1678,10.基金项目
国家自然科学基金项目(No.61175087) （No.61175087）
北京工业大学智能机器人"大科研"推进计划"助老智能轮椅床自主测控系统的研究与实现"资助项目(No.040000546317552) （No.040000546317552）
