Satellite videos capture the dynamic changes in a large observed sense, which provides an opportunity to track the object trajectories. However, existing multiple object tracking (MOT) methods require massive video annotations, which is time-consuming and fallible. To alleviate this problem, this article proposes a cross-domain multiple object tracker (CDTrack) to learn knowledge from multiple source domains. First, a cross-domain object detector with multilevel domain alignment is constructed to learn domain-invariant knowledge between remote sensing images and satellite videos. Second, the proposed method adopts a bidirectional teacher-student framework to fuse multiple source domains. Two teacher-student models learn different domain knowledge and teach mutually each other. With mutual learning, the proposed method alleviates the discrepancies between different domains. Finally, a simple weakly supervised Re-IDentification (Re-ID) model is proposed for long-term association. Experimental results on the satellite video datasets demonstrate that the proposed method can achieve great performance without satellite video annotations. The code is available at https://github.com/XiangtaoZheng/CDTrack. © 1980-2012 IEEE.