Action Recognition from Videos with Complex Background via Transfer Learning  

作　　者：林贤明 李绍滋 张洪博 刘姝 

机构地区：[1]Department of Cognitive Science,Fujian Key Laboratory of the Brain-like Intelligent Systems,Xiamen University

出　　处：《Journal of Donghua University(English Edition)》2010年第2期199-203,共5页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China ( No.60873179);Shenzhen Municipal Science and Technology Planning Program for Basic Research, China ( No. JC200903180630A);Research Fund for the Doctoral Program of Higher Education of China (No.20090121110032)

摘　　要：Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However,in many practical situations,this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kungliga Tekniska Hgskolan (KTH). When training data are very scarce,supervised learning is difficult. However,it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper,we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm,we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.

关 键 词：action recognition transfer adaboost learning maximum mutual information 

分 类 号：F391[经济管理—产业经济]