Multiple　object　tracking　(MOT)　plays　a　key　role　in　video　analysis.　On　MOT,　DeepSORT　(Simple　Online　and　Realtime　Tracking　with　a　deep　association　metric)　performs　effectively　by　combining　features　of　appearance　and　motion　for　estimating　data　association.　However,　computing　with　multiple　features　are　time　consuming.　In　certain　applications,　cameras　are　static,　such　as　pedestrian　surveillance,　sports　video　analysis　and　so　on.　Here,　without　camera　movement　the　motion　trajectories　of　objects　are　generally　possible　to　estimate.　The　introduction　of　more　features　cannot　improve　the　performance　of　object　tracking　discriminatively.　Furthermore,　the　time　cost　rises　evidently.　To　address　this　problem,　we　propose　a　novel　Simple　Online　and　Realtime　Tracking　with　motion　features　(MF-SORT).　By　focusing　on　the　motion　features　of　the　objects　during　data　association,　the　proposed　scheme　is　able　to　take　a　trade-off　between　performance　and　efficiency.　The　experimental　results　on　the　MOT　Challenge　benchmark　and　MOT-SOCCER　(newly　established　in　this　work)　demonstrate　that　the　proposed　method　is　much　faster　than　DeepSORT　with　the　comparable　accuracy.　©　2019,　Springer　Nature　Switzerland　AG.