Research On Video Behavior Recognition Algorithm Based On Deep Learning

In recent years,human behavior recognition in videos has received more and more attention and application.Its reasons are as follows:first,there are major achievements in research about deep learning and pattern recognition;second,with the rise of smart-city and smart-home,the intelligent development of society provides a broad application prospect for human behavior recognition.Therefore,how to recognize human behavior in video quickly and accurately is the most common research at present.The essence of human behavior recognition is a multi-classification problem,focusing on a series of analysis of human motion behavior in video frames.Ultimately,the judgment of human behavior is determined by appropriate classification methods.This paper starts from the traditional machine learning-based behavior recognition technology,introduces deep learning-based human behavior recognition,and studies and compares the two methods,combined with their performance in practical applications.Improvement of human behavior recognition method based on deep learning.The main research work of the paper is as follows:1.Briefly reviewed the development of machine learning-based behavior recognition technology,introduced several typical machine learning algorithms and summarized their advantages and disadvantages.This article reviews the behavior recognition technology based on deep learning,reviews its development history,and analyzes its video timing modeling ideas.2.The key of video behavior recognition lies in the modeling of long-term temporal structure.In this dissertation,the classic dual-stream convolutional neural network is used as the basic network,and the time-segmentation network TSN is obtained by improving it.The idea of long-term timing modeling of TSN network is to segment sparse samples.This method can efficiently display the long-term timing information of video.The contribution of this paper is to propose a multi-time-scale sliding window sampling method based on the original time-segmented network to solve the problem of small proportion of effective video frames in some videos and achieve better behavior recognition.3.For the spatio-temporal characteristics of the video,the temporal and spatial characteristics of the video are first extracted during the experiment,and then the spatio-temporal characteristics are obtained by fusion.On this basis,this thesis introduces the human attention mechanism into the spatiotemporal fusion model,and obtains the spatiotemporal fusion characteristics based on the attention mechanism.The datasets used in this paper are public datasets commonly used in behavior recognition,UCF101 and HMDB51 datasets.The entire experiment is based on the deep learning framework TensorFlow.From the experimental results,the dual-stream convolutional neural network based on multiple time scales and the spatial-temporal fusion model based on attention mechanism proposed in this paper have achieved good recognition for video behavior recognition effect.