Research On Human Behavior Detection Technology In Infrared Thermal Image

Human behavior detection in infrared thermal imaging images refers to analyzing the behavior and actions of human targets in the infrared thermal imaging image data and generating description statements in line with the user's browsing.The infrared thermal imaging data includes video,series of images and single frame images.This technology has a wide application prospect in the fields of dangerous behavior detection,image retrieval,human-computer interaction,intelligent care and so on.Therefore,it is gradually being studied by more academic and industrial researchers.The current human behavior detection algorithm has a lot of redundant data in the original data and fails to take advantage of the correlation between human behavior and the surrounding target,the continuity of human behavior and the time sequence between multiple images.In order to solve the above problems,this paper proposes a human behavior detection algorithm in infrared thermal imaging images.Based on infrared thermal imaging images,combined with deep learning algorithm,the human behavior detection technology is studied to realize human behavior detection in infrared thermal imaging images.The main research contents of this paper include:(1)Aiming at the problems of data redundancy and many images unrelated to human behavior in the currently used infrared thermal imaging data,an image key frame extraction algorithm based on an improved Siamese network is proposed.First,use the improved VGGNet-16 network for this article to replace the Alex Net structure in the original Siamese network,change the data input mode,adopt the 2-channel input mode,and input two adjacent frames at the same time,and use the VGGNet-16 network to extract the feature information of the image.Use the 1000-dimensional information of the last fully connected layer of the network to calculate the image similarity,and realize the preliminary classification of the original image based on the preset double threshold;then,use the improved YOLO v3 network model to realize the target recognition in the image,and use the target Identify the information,filter the preliminary classified images,and complete the final image key frame extraction;the improved VGGNet-16 network and the YOLO v3 network form a parallel structure,which reduces the model depth of the proposed algorithm.Experimental results show that the algorithm can effectively solve the data redundancy problem,The extracted key frames are more targeted,The average accuracy rate on different data sets is 92.5%,the average extraction time is 22.5s,and the key frame extraction speed and accuracy rate are significantly improved.This is the follow-up work of this article.Lay a good foundation for the progress.(2)Aiming at the current human behavior detection algorithms in infrared thermal imaging images that do not use the correlation between human behavior in the image and surrounding targets,the sequence of images,and the detection description is too simple,a human body based on an improved visual bag-of-words coding model is proposed.Behavior detection algorithm.First,the attention mechanism model is used to realize the initial assignment of the weights of different targets in the image for human behavior detection;then,the target spatial position information obtained by the improved YOLO v3 network is used to update the weight information of different targets to maximize the use of the human body Target information related to the behavior.Based on the improved visual bag-of-words model,the encoding of image vocabulary information is completed;finally,through the sequence information of the series of images,the bidirectional long-term short-term memory network(Bi-LSTM)is used for decoding,and the KL divergence and artificially annotated data tags are used to construct The loss function is used to train the network.The experimental results show that the human behavior description sentences generated by the algorithm are basically consistent with the image information,the average precision rate on the data set is 91.0%,and the detection speed is faster;the key frame extraction algorithm proposed in this paper can further improve the human body The performance of the behavior detection algorithm.