At　present,　vehicle　tracking　has　been　realized　in　many　fields.　The　applications　of　vehicle　tracking　include　Advanced　Driver　Assisted　System　and　live　broadcast　of　automobile　events.　After　reading　the　paper　＇　Intelligent　vehicle　pedestrian　tracking　based　on　YOLOv3　and　DASiamRPN　＇.　We　learn　that　in　this　paper,　they　propose　a　pedestrian　tracking　algorithm　which　combines　YOLOv3　and　DASiamRPN　to　realize　the　designated　pedestrian　tracking.Finally,　the　pedestrian　tracking　is　successfully　realized.　Which　ensures　the　tracking　accuracy.　However,　while　exploring　the　guarantee　of　accuracy,　there　are　few　studies　on　how　to　achieve　successful　tracking　at　different　tracking　speeds.The　most　difficult　part　about　vehicle　tracking　is　fast　vehicle　tracking.　High　speed　often　makes　the　system　lose　tracking　target.　So　in　this　paper,　we　studied　how　to　implement　fast　vehicle　tracking.　However,　there　are　many　algorithms　for　vehicle　tracking,　such　as　the　Lucas-kanade　algorithm,　Faster　RCNN　algorithm,　Yolo　algorithm,　SSD　algorithm,　and　DeepSORT　algorithm.　More　and　more　new　algorithms　have　appeared　in　recent　years.　But　through　experiments,　we　found　that　there　are　some　vehicle　tracking　algorithms　such　as　the　Lucas-kanade　algorithm,　which　cannot　achieve　accurate　vehicle　tracking　in　the　case　of　rapid　vehicle　movement.　Therefore,　we　listed　and　studied　a　variety　of　algorithms　for　vehicle　tracking.　After　querying　the　papers.,　comparing　and　analyzing,　one　of　the　appropriate　algorithms　was　selected:　DeepSORT　and　Yolo-V5　algorithm.　By　analyzing　the　principles　and　image　processing　flow　of　DeepSORT　and　Yolo-V5　algorithm,　we　know　that　this　algorithm　is　updated　and　more　reliable.　After　experiments,　it　successfully　tracks　fast　moving　vehicles　in　a　given　video.　After　statistics,　the　successful　tracking　time,　testing　time　and　accuracy　of　the　video　meet　the　successful　tracking　standard.　©　2022　SPIE.