Transformer can build global relationships between pixels and enhance pixel representation. The existing methods only establish the context relationship from the whole image but will reduce the representation between the category areas. In addition, the existing methods based on the transformer self‐attention do not combine the advantages of convolution and transformer, resulting in more calculation parameters of the model. In order to solve these two problems, this paper proposes to enhance the segmentation accuracy and performance by enhancing the relationship between image‐level regions and the relationship between semantic level pixels. First, we design a refined division feature (RDF) module to enhance the channel representation and thus the same locale representation. Second, we design a transformer based on convolution (CTrans), which first computes the relationship between similar pixels and enhances the pixel representation. Then, the feature map is compressed and enriched to reduce the computational load of CTrans, and finally the relationship between pixels is established from a global perspective. We design a refined division feature module based on transformer for semantic image segmentation (RFT) model combining RDF and CTrans module. The experimental results show that the mIoU result of our method in Cityscapes test data set is 81.9%, and the model parameter is 64.6M, which is superior to other methods in terms of data. In addition, we conducted visualization experiments with Cityscapes and Pascal voc 2012 datasets with other methods, and the results showed that our method was superior to other methods.

With the advancement of Internet of Things (IoT) and artificial intelligence technologies, and the need for rapid application growth in fields, such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. However, in the process of acquiring facial feature information, face information is easily affected by factors, such as object occlusion, lighting changes, and similar backgrounds. In this paper, we propose a multifeature fusion algorithm based on integral histograms and a real-time update tracking particle filtering (PF) module. First, edge features and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour features and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the PF algorithm to simplify the calculation steps of complex particles and improve operational efficiency. Finally, the tracking window size is adjusted in real-time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness compared with extracting a single feature.