The　online　multiobject　tracking　(MOT),　which　integrates　object　detection　and　tracking　into　a　single　network,　has　made　breakthroughs　in　recent　years.　However,　most　online　trackers　have　a　more　monotonous　prediction　of　the　tracking　offset　in　two　consecutive　frames,　which　may　not　be　reliable　when　facing　extreme　situations　such　as　occlusion　and　object　deformation.　Once　the　tracking　offset　of　an　object　is　biased,　its　corresponding　tracklet　will　no　longer　maintain　temporal　consistency,　which　will　seriously　affect　the　tracking　performance.　In　this　paper,　we　propose　a　new　online　multiple　object　tracker　with　feature　enhancement　mechanism,　namely　En-Tracker.　In　En-Tracker,　a　multibranch　kinematic　analysis　network　(MKANet)　is　designed　to　address　the　above　problems.　MKANet　estimates　the　pixel　offset　and　instance　offset　of　the　object　in　parallel　based　on　imitating　the　human　thinking　to　joint　position　and　appearance　representations.　Note　that　these　two　types　of　offsets　compensate　and　facilitate　each　other　to　effectively　deal　with　some　extreme　scenarios.　In　addition,　we　propose　a　kinematic-assisted　feature　synthesis　enhancement　(KFSE)　module,　which　has　a　more　comprehensive　enhancement　mechanism.　Specifically,　KFSE　propagates　previous　tracking　information　to　the　current　frame　based　on　kinematic　trend　analysis　while　enhancing　the　characterization　of　detection　features　and　appearance　embeddings,　which　not　only　assists　in　object　detection　but　also　ensures　the　uniqueness　of　the　appearance　embeddings.　Extensive　experiments　on　MOT16　and　MOT17　verify　the　effectiveness　and　advantage　of　our　model.　　©　2023　SPIE　and　IS&T.