Modified CRF Algorithm for Dynamic Hand Gesture Recognition

摘要：

　　In this paper,a modified CRF algorithm is proposed for recognition of vision-based dynamic hand gestures.This algorithm abandons the condition necessary for Hidden Markov Models that the action sequences must be independent.And dynamic hand gestures are classified by some most representative segments(MRSs)rather than the full gestures themselves.First,the Longest Common Sequence(LCS)is employed to extract the most representative segments from dynamic gestures which are then used to train Conditional Random Fields(CRF).In a recognition stage,MRS of the unclassified trajectory is sent to CRF.Experiment results show that this algorithm(defined as MRS-CRF)has significant advantages over HMMs in accuracy and CRF itself in simplification.

关键词： Dynamic hand gestures Most representative segment (MRS) CRF

作者: Liling Ma Jing Zhang Junzheng Wang

作者单位: Beijing Institute of Technology

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4763-4767

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）