Human hand trajectory prediction and intention recognition method based on multi-feature fusion

Abstract

The invention discloses a human hand trajectory prediction and intention recognition method based on multi-feature fusion. The method comprises the following steps: acquiring face and shoulder key point data of a person; acquiring palm track data; inputting the face and shoulder key point data of the person into a support vector machine to obtain face orientation modal information; inputting the palm trajectory data sequence into an SG filter, and eliminating trajectory data fluctuation to obtain smooth trajectory data; carrying out parallel fusion on the two types of modal information to obtain multi-modal fusion information; and inputting into the LSTM network, and outputting a predicted trajectory of the palm. According to the method, the face orientation features are extracted by using part of the face key points, the face orientation features are further fused with the human arm trajectory data, the moving trajectory of the human arm in the space and the final arrival position of the human arm are predicted, and the moving trajectory is efficiently and accurately predicted.

Description

technical field [0001] The invention belongs to the field of visual recognition, and in particular relates to a method for predicting and recognizing human hand trajectory based on multi-feature fusion. Background technique [0002] Due to the nonlinearity, randomness, and diversity of external and internal stimuli, it is difficult to accurately predict human motion; however, for workers on the assembly line, their motion behavior is mainly determined by task goals and Part layout-driven, making good predictions with this contextual information. However, the working environment may be relatively complex, or the distribution of installation parts and tools may be chaotic and disorderly, and it is often not enough to recognize and predict the position and trajectory of the human hand. For example, in the installation scene, workers usually straighten up or stand up in order to pick up parts that are far away, the starting position of the arms is relatively high, and the hand ...

AI Technical Summary

Problems solved by technology

However, the working environment may be relatively complex, or the distribution of installation parts and tools may be chaotic and disorderly, and it is often not enough to identify and predict the trajectory of human hands

For example, in an installation scenario, workers usually straighten up or stand up in order to pick up parts that are far away. Parts only need to be grasped by raising the hand; the grasping behavior (action and stroke) of these two types of parts are completely different, and only using the arm position for trajectory prediction will lead to unstable efficiency

Images