Using dynamic programming to match human behavior sequences
Yan Chen, Qiang Wu, Xiangjian He · 2008
This paper proposed a new approach for recognition and matching the human behavior sequence. Each human behavior sequence is represented by its key postures to greatly reduce the computation time. Normalization is applied to all the behavior sequences key postures for matching. A dynamic time warping (DTW) algorithm is used to perform the alignment of two time series. Experiments are carried out on an open human behavior database and exciting results have been obtained.