@inproceedings{885ed9343a824af4932cdd79db3a4844,
title = "Modified CRF algorithm for dynamic hand gesture recognition",
abstract = "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.",
keywords = "CRF, Dynamic hand gestures, Most representative segment (MRS)",
author = "Liling Ma and Jing Zhang and Junzheng Wang",
note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",
year = "2014",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2014.6895744",
language = "English",
series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
publisher = "IEEE Computer Society",
pages = "4763--4767",
editor = "Shengyuan Xu and Qianchuan Zhao",
booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
address = "United States",
}
