In the task of unmanned vehicle infrared video colorization, considering the uniqueness of a single frame and the continuity of the entire infrared video, a dual-channel cycle-consistent adversarial network (DcCCAN) based colorization method is proposed. The dual-channel generation network we proposed is on the basis of cycle-consistent adversarial network (CCAN) and has good image feature extraction ability, which can automatically extract the features of the frame in the video, and at the same time can extract the features of the previous frame generated. By joint training of adversarial loss and cycle-consistent loss, the function from infrared domain image to color domain image can be learned by unsupervised learning methods and the colorization of the infrared video can be realized. The experimental results show that the proposed method can provide natural color information and texture information for the infrared images in the video, and meet the real-time requirements.