Aiming at the problems such as complicated flow

high computational cost and insufficient feature learning that traditional saliency detection methods have

inspired by generative adversarial networks and Elastic networks

a video salient object detection method based on conditional generative adversarial networks（c GAN）combined with L1 and L2 paradigms is proposed. The proposed method requires to train two models: generator and discriminator. As much as possible

the generator generates the same saliency map as the truth value to confuse the discriminator

making it difficult to discern the authenticity of the predicted saliency map. The discriminator

on the other hand

distinguishes the"false"saliency map as accurately as possible. Experiments show that detection effect of the method outperforms the current mainstream methods in both open video datasets

and the algorithm has simple flow and higher efficiency.