Research On Temporal Action Detection And Action Recognition Based On Deep Learning

In recent years,with the breakthrough in computer hardware technology,people begin to study the application of deep learning technology in life,and increasingly rely on science and technology,and more and more video data are available on the network.Video is an important communication medium of visual information,and video analysis is also an important topic of Internet technology.It has made important contributions in security,traffic,airport monitoring and other aspects.In the video,the information of human movements is often analyzed,so the study of action recognition has been pushed into a boom.In real life,videos often contain a large number of irrelevant background clips.The simple action recognition algorithm can only classify the actions in the video,but it is not capable of practical application.Therefore,the temporal sequence action location task is introduced after action recognition,aiming to find the time range of the actions in the video.For a long background video,it is necessary to first locate the temporal sequence action and then identify the behavior.The main work of this paper is as follows:(1)In order to ensure a high recall rate,most of the existing human temporal action detection algorithms rely too much on behavioral features,have a low ability to process features,and are not sensitive to the boundary of temporal sequence positioning.For such problems,based on the boundary sensitive network,probability curve modeling was carried out on the video features,and in the internal boundary assessment model,by building a low-level two-stream characteristics,reduce dependence on video feature extraction phase algorithm,without any increase in increase the rate of recall,on the basis of model layer and reference in the field of target detection principle,improve the maximum suppression algorithm,for these are ignored in the final stages of temporal information the coordinates of the weighted average score,has realized the integration model of the self.(2)The two-stream convolution based network in the field of action recognition has high accuracy,in order to continue to improve the accuracy and the deepening of the network method,this article through the replacement of space and time layer network test comparison,using Res Net can at the same time of deepen the network layer without increasing model has the advantages of the computing complexity and layer network to replace the original space,time replaced by BN-Inception network layer comparison proved that after the replacement of heterogeneous network to homogeneous network effect is better than the original.Finally,experimental comparison on UCF101 and HMDB51 data sets proves the superiority of the proposed model.(3)Design a monitoring video fall detection system for nursing homes that can run on the PC.Each function module of the system is introduced in detail,and Python and Qt are used to complete the human-computer interaction interface,model call and background program processing.Finally,the video of MCFD fall data set is used to test and verify the system in this paper,which proves its feasibility and effectiveness.