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.