Continuous motion recognition method based on improved viterbi algorithm

Abstract

The invention belongs to the field of video processing, and particularly relates to a continuous motion recognition method based on an improved viterbi algorithm. The continuous motion recognition method based on the improved viterbi algorithm is used for efficiently recognizing continuous motions of a whole human body. The method comprises the steps that firstly, motion differences caused by factors such as the rotation and translation of a person are eliminated through building a coordinate system which is benchmarked against human joints; secondly, further processing is performed on a human skeleton coordinate according to the constraint of a human model to eliminate differences caused by the human body configuration; thirdly, encoding is performed on human joint point data through K-means; fourthly, corresponding hidden Markov models are trained for all motions; fifthly, segmentation of continuous motion sequences is performed according to the changes of the active joints, the speed and the size of angle; sixthly, the better candidate motion is filtered for each subsequence; finally, a best path is found out through the improved viterbi algorithm, and the best motion sequence is obtained. Compared with an existing method, the method can efficiently recognize the motions contained in the continuous motion sequences.

Description

technical field [0001] The invention belongs to the field of video processing, and in particular relates to a continuous motion recognition method based on an improved Viterbi algorithm, which is used for efficiently recognizing continuous motions of the whole body of a human body. Background technique [0002] With the rapid development of computer applications, human behavior recognition based on video sequences has gradually become a research hotspot in the field of computer vision, and has been widely used in human-machine interface systems, smart home applications, intelligent monitoring, and motion analysis. [0003] Scholars at home and abroad have done a lot of basic research on the problem of action recognition, which has high application value. Commonly used action recognition methods are: HMM-based methods, DTW-based methods, SVM-based methods, and neural network-based methods. For single action recognition, these methods have achieved relatively satisfactory res...

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Problems solved by technology

In this way, from the perspective of the observer, only the observation value can be seen, unlike the one-to-one correspondence between the observation value and the state in the Markov chain model

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