A Behavior Recognition Method Based on Motion History Images and Convolutional Neural Networks

Abstract

The invention discloses a behavior recognition method based on a motion history image and a convolutional neural network, comprising the following steps: S1, acquiring an input original video image, and processing it through a behavior sequence feature extraction method based on a motion history image; S2 1. Using a method based on a deep convolutional neural network to conduct behavior recognition on local motion history images to obtain a behavior category classifier, and finally output the behavior recognition result through the behavior category classifier. The present invention calculates motion history images from original video sequences, which not only reduces the amount of information to be processed, but also extracts key spatio-temporal information in behavior recognition; then uses motion history images as input to build a deep convolutional neural network, and then uses Stochastic gradient descent (SGD) and Dropout strategy train the network, and finally realize the classification of behavior categories. The invention can be effectively applied to online real-time behavior recognition.

Description

technical field [0001] The invention relates to the field of behavior recognition, in particular to a behavior recognition method based on motion history images and convolutional neural networks. Background technique [0002] Human behavior recognition technology based on computer vision is widely used in robotics, video surveillance, virtual reality and other fields. The methods to solve human behavior recognition problems are mainly divided into traditional algorithms and recognition algorithms based on deep learning. The traditional algorithm uses the method of "feature extraction and expression + feature matching" to recognize human behavior, while the recognition algorithm based on deep learning learns the characteristics of the object through the neural network and directly outputs the final recognition result. At present, a lot of research focuses on improving the accuracy rate, ignoring the real-time nature of the algorithm, and in various practical applications, the...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to solve the defect that the accuracy of behavior recognition using traditional feature descriptors as a feature expression method in the prior art is low, and that directly using the original video as the input of the neural network will bring about a decrease in algorithm speed, and provide A behavior recognition method based on motion history images and convolutional neural networks. The present invention uses a deep learning method based on the adjusted AlexNet network for behavior recognition to improve the accuracy of the algorithm, and uses motion history images as the input of the neural network to improve the accuracy of the algorithm. algorithm speed

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