Transfer AdaBoost Learning for Action Recognition

摘要：

The universal dataset of human action such as KTH includes only simple background, in which the action videos are much different to practical action videos. So the accurate rate of action recognition on practical videos always not so good as on our test videos from the training dataset. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with various backgrounds. In this paper, we propose a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms. By using this algorithm, we can train a action recognition model fitting for most practical situations just relaying on the universal action video dataset and a little set of new action videos with complex background. And by using the TrAdaBoost, the generality of our action recognition model is greatly improved.

作者: LIN Xian-ming LI Shao-zi

作者单位: Department of Cognitive Science, Xiamen University, Xiamen, 361005, China Department of Automatization, Xiamen University, Xiamen, 361005, China

会议类型: 国际会议

会议名称: 2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)

会议地点: 济南

会议语种:英文

页码: 659-664

在线出版日期: 2009-08-14（万方平台首次上网日期，不代表论文的发表时间）