Criminal Identification from Video Scene Using Conditional Generative Adversarial Network

Abstract

Criminal identification from a blurry image is a challenging task from a video scene. Many criminals are unidentified due to blur degradation or additional noise in videos. This paper introduces a novel approach of identifying the perpetrators using conditional Generative Adversarial Network. This network figures out how to distinguish the criminals, yet additionally, it learns a loss function for training the model. In this method, our model evaluates from a self-created dataset where the video frames are blurred or unclear. This work demonstrates how to detect criminals from the blurred dataset using cGAN. The conditional model of GAN makes the generator stronger to produce clear images from unclear data after a specific number of iterations. Then, the trained stable discriminator able to distinguish the real criminal. The proposed method performs significantly in an efficient way to identify the criminal which might be a revolutionary approach for the welfare of the society.