Human Action Recognition Based On Image Super-Resolution And Visual Saliency

With the development of Internet technology and the upgrading of large storage devices,the number of video on the network have a explosive increase,meanwhile,the requirements of video processing technology are constantly improving.How to process video data and identify video information with computer efficiently has become an urgent problem needed to be solved.Because of most of the videos are based on human,it is particularly important to study human behavior recognition technology.Traditional human behavior recognition methods rely on pattern recognition and matching technology mainly,which not only has low recognition flexibility,but also requires a lot of labor costs.With the rise of artificial intelligence technology,human behavior recognition which based on artificial intelligence technology has become a research hotspot.In order to improve the recognition accuracy of human behavior recognition method which based on artificial intelligence technology and reduce the hardware requirements and training costs of human behavior recognition algorithm,this paper mainly carries out the following research contents:(1)Constructing human behavior recognition neural network by transfer learning.Training the neural network based on UCF-101 data set,and optimizing the output results by fine-tuning the non-common feature layer.Finally,obtaining the human behavior recognition model.(2)In order to solve the problem of local optimal solution without increasing the batch processing step size,a super-resolution image reconstruction method based on FSRCNN is introduced.Through super-resolution reconstruction of the original images,the information contained in the original images is increased,so that the loss function can converge rapidly with low processing step size,by what improving the recognition accuracy and reduce the dependence of network on hardware configuration.(3)In order to solve the problem of slow training speed of human behavior recognition model,FrFT visual saliency detection method is introduced in this paper.By detecting the key targets in the images in advance,the recognition of the whole images will be converted to the recognition of the key targets.In this method,the influence of complex background on the recognition will be restrained,and the information that the recognition algorithm needs to process will be reduced,Finally,reducing the training time of the neural network.The experimental results shown that these methods greatly improved the accuracy of human behavior recognition under the same batch processing step,and reduced the requirement of hardware configuration,the requirement of original videos' clarity and the training time of the neural network,which provides support for the development of human behavior recognition technology.