Adaptive Adjustment Object Detection Algorithm under Multiple Mechanisms Based on GAN

Abstract

Target tracking is prone to problems such as target loss and identity jump when the target is occluded and the attitude is changed. In order to solve this phenomenon, this paper proposes the adaptive adjustment object detection algorithm under multiple mechanisms based on GAN. This algorithm introduces a gradient penalty mechanism to the discriminator and uses the relative discriminator structure to reconstruct the discriminator, so as to improve the discriminatory ability of the discriminator. Then, through the feedback mechanism, the obtained data is fed back to the generator network in time, and the genetic mechanism is used to speed up the positioning of the key areas of the image. Experimental results show that compared with other existing algorithms, this algorithm can effectively locate and distinguish under different environments. And, the target still maintains a high resolution. When the target is occluded, it can effectively avoid the phenomenon of target loss and identity jump.