Abstract:  This study focuses on the problems that the traditional human posture recognition data acquisition is easily disturbed by environment, and it’s difficult to solve the similarity of human motion postures and the characteristics difference of the human motion executor. This paper proposes a human posture recognition method based on few key frames sequence. Firstly, the original motion sequence is pre-selected. The initial key frame sequence is constructed by taking the extremum of the motion trajectories, and the final key frames sequence is obtained by using frame subtraction algorithm. Then, we built the hidden Markov model for different human postures and trained the model. The Baum-Welch algorithm is used to calculate the initial probability matrix, the confusion matrix and the state transition matrix, and the post-training model is obtained. Finally, the probabilities for each model are achieved by inputting the measured data and applying forward algorithm, and the gestures corresponding to the maximum probability are compared and selected as what is identified. Experiment results show that our method can efficiently select key frames of the original motion sequence, and effectively improve the accuracy of human body gesture recognition.

Key words: human posture recognition, frames sequence, frame subtraction, hidden Markov model