Fast Target Recognition Method Based on Multi-Scale Fusion and Deep Learning

Abstract

Under the natural state of unrestricted conditions, the accuracy of effective recognition of image target information captured by ordinary cameras is significantly reduced. At present, the mainstream research methods for image target recognition focus on the processing of images based on algorithms with strong ability to describe image target features so as to improve the image target recognition performance in cases of complex noise interference. However, most of the methods cannot adapt to various changes in the background when the environment changes. Therefore, this article conducts studies on the fast target recognition method based on multi-scale fusion and deep learning. The optimized local binary pattern algorithm and the HOG algorithm are used to extract the image target features, the dimension reduction of the extracted image target features is carried out based on the generalized discriminant analysis algorithm, and multi-scale fusion of image targets is accomplished based on the discriminant correlation analysis. The experimental results verify the effectiveness of the proposed algorithm.

Review on Millimeter-Wave Radar and Camera Fusion Technology

“…However, when the number of targets and measurements increases, the JPDA algorithm will experience a combinatorial explosion in computational effort, which results in computational complexity. After acquiring the target sequence, Sun et al [76] introduced the Marxian distance for observation matching based on the target-level fusion method. Then the JPDA was applied for data fusion to establish the system observation model and state model, thus realizing the target recognition based on information fusion.…”