Video object segmentation method based on self-paced weak supervised learning

The invention provides a video object segmentation method based on self-paced weak supervised learning. A self-paced learning algorithm is embedded into a depth neural network, under the guidance of the thought of weak supervised learning, a whole system learns target concepts from the easier to the more advanced, the network obtained by learning with the training process becomes complex, the ability of the network to deal with problems is gradually increased, and finally, an accurate video object segmentation result is obtained. The invention utilizes the advantages of the self-paced learning algorithm and the deep neural network model comprehensively, has higher segmentation accuracy, and shows better robustness when processing video data of different scenes.