An Attention-Free Capsule Network-Based Employee Re-recognition Method for Factory Surveillance Images with Network Slicing

Abstract

Traditional methods have low recognition rate and poor robustness when dealing with complex factory employee monitoring images, so a new employee re recognition method based on inattentive capsule network for factory monitoring images is proposed. Firstly, LSGAN is used to restore the factory monitoring image to repair the missing or damaged image caused by lighting, occlusion, noise, etc; Secondly, wavelet Contourlet transform is used to improve the details and clarity of images and the accuracy of subsequent staff re recognition; Finally, the mixed Gaussian model (GMM) is used to accurately segment the employee foreground in the image, and the segmented employee foreground image is input into the no attention capsule network. The feature is extracted through multi-layer convolution and pooling operations, and the dynamic routing mechanism is used to extract and aggregate employee identity features. After training, the employee identity tags are output to achieve efficient employee re recognition of factory monitoring images. Experimental results show that the proposed method has strong image processing ability, high recognition accuracy and robustness.