||A Recognition Algorithm for Public Security Images
|| LabVIEW; Epidemics; Sudden events; Public security images; Image recognition
||To improve the efficiency of public security image inspection and monitoring during epidemics and sudden events, an image recognition algorithm based on LabVIEW is proposed. By using fusion detection for human appearance parameters and dynamic gaits, a block feature-matching model for suspicious dynamic information in security images is constructed. Considering the environmental factors, security images with suspicious background information taken during epidemics and sudden events are decomposed, and edge contour detection is constructed. Based on suspicious edge detection results, the spatial structure of the images is extracted, risk difference characteristics of human body shapes in the images are captured, and a variety of local and global feature analysis models for public-place security images based on epidemics and sudden events are constructed. Through cross-regional block fusion and by using gray edge information decomposition, the suspicious features extracted from security images captured during epidemics and sudden events are realized, and visual simulations are carried out based on LabVIEW. The results show that the proposed method has high output recognition, good fusion performance on safety factors, an improved recognition ability with security images, and good performance in feature detection from security images.