Action Recognition of Human’s Lower Limbs Based on a Human Joint

Abstract

To recognize the actions of human's lower limbs accurately and quickly, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and human action characteristics were achieved based on Butterworth filter and wavelet transform. Secondly, an improved self-organizing competitive neural network was proposed, which could classify the action characteristics automatically according to the classification number. The classification results of motion capture data proved the validity of the neural network. Finally, an action recognition method based on hidden Markov model (HMM) was introduced to realize the recognition of classification results of human action characteristics with the change direction of y coordinates. The proposed action recognition method needs less action information and has a fast calculation speed. Experiments proved the method had a high recognition rate and a good application prospect.

Infrared Human Posture Recognition Method for Monitoring in Smart Homes Based on Hidden Markov Model

“…original image after the binary processing. Equation (22) Remove the image pixels that do not meet the conditions. In this way, the boundary of the object will shrink into the interior.…”

“…The set X expresses the pixel collection of the original image after the binary processing. Equation (22) describes the erosion of X by structure element B to get S. The set S is formed by all elements of B which are completely contained in X.…”

“…In 2015, Liang [22] presented an action recognition method on the motion process of a human's lower limbs. According to the motion characteristics of the human's lower limbs, motion parameters used for action recognition were chosen.…”

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