东南大学学报（自然科学版）Issue(3)：493-498,6.DOI:10.3969/j.issn.1001-0505.2014.03.008
基于多特征融合和分层反向传播增强算法的人体动作识别
Human action recognition based on multi-feature fusion and hierarchical BP-AdaBoost algorithm
摘要
Abstract
To popularize the application of neural network in human action recognition,an action recognition system based on the hierarchical recognition framework and the boosting algorithm is de-signed,which mixes together multiple features such as histograms of optical flow,histograms of ori-ented gradients,Hu’s moments,block-silhouettes and self-similarity matrices.To fit with the boos-ting of back-propagation (BP)networks,the standard binary AdaBoost algorithm is extended to a multiclass version.Besides,this system adopts a hierarchical recognition framework consisting of pre-decision and post-decision.The former can roughly classify the actions into several subcategories by analyzing the locations of motion salient regions,whereas the latter exploits extra features to fur-ther enhance recognition accuracy.The experimental results on Weizmann and KTH datasets show that neural networks exhibit obvious advantages over the popular support vector machine.The BP-AdaBoost algorithm combined with hierarchical recognition can greatly reduce the computational cost and confusions among actions to achieve high recognition accuracy.关键词
特征提取/动作识别/反向传播增强算法/神经网络/分层识别Key words
feature extraction/action recognition/back-propagation (BP)-AdaBoost algorithm/neural network/hierarchical recognition分类
信息技术与安全科学引用本文复制引用
李拟臖,程旭,郭海燕,吴镇扬..基于多特征融合和分层反向传播增强算法的人体动作识别[J].东南大学学报（自然科学版）,2014,(3):493-498,6.基金项目
国家自然科学基金资助项目（60971098）、国家自然科学基金青年基金资助项目（61302152）． （）
