Behavior recognition system and method based on multi-convolution kernel residual network, medium and equipment

Abstract

The invention provides a behavior recognition system and method based on a multi-convolution kernel residual error network, a medium and equipment, and the method comprises the steps: collecting the motion physiological data or environment state data of a user, and sequentially carrying out the transmission, preprocessing and marking of the collected data to obtain a training set and a test set; constructing a behavior recognition model based on the multi-convolution kernel residual error network; classifying the training samples according to the prediction error of the training set and carrying out self-adaptive weight distribution on each class; calculating an overall loss function and partial derivatives of each parameter of the model, iteratively updating the model parameters to obtaina trained recognition model, testing by using the test set, storing the model parameters reaching the test standard, and generating a final deep learning model; inputting to-be-recognized data into the deep learning model to obtain a user behavior recognition result. According to the invention, multi-scale data features can be effectively utilized.

Description

technical field [0001] The disclosure belongs to the technical field of artificial intelligence and pattern recognition, and relates to a behavior recognition system, method, medium and equipment based on a multi-convolution kernel residual network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The behavior recognition system realizes the recognition of user behavior by obtaining human behavior information and processing it through models and algorithms. With the development and maturity of advanced technologies such as the Internet of Things, artificial intelligence, big data, and cloud computing, more and more scholars have begun to pay attention to the research of behavior recognition. At the same time, the development of wearable devices provides a good opportunity for user behavior recognition, and behavior recognition has become ...

AI Technical Summary

Problems solved by technology

[0006] The data collected by the sensor is usually a time series. Some sampling points may have a long distance before and after the time, but the correlation in the time series is very large. Therefore, it is difficult to extract the time before and after the time when fixed small-size convolution kernels are widely used at present. Correlation features for data at greater distances

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