Object　tracking　is　an　important　research　direction　in　computer　vision　and　is　widely　used　in　video　surveillance,　security　monitoring,　video　analysis　and　other　fields.　Conventional　tracking　algorithms　perform　poorly　in　specific　scenes,　such　as　a　target　with　fast　motion　and　occlusion.　The　candidate　samples　may　lose　the　true　target　due　to　its　fast　motion.　Moreover,　the　appearance　of　the　target　may　change　with　movement.　In　this　paper,　we　propose　an　object　tracking　algorithm　based　on　motion　consistency.　In　the　state　transition　model,　candidate　samples　are　obtained　by　the　target　state,　which　is　predicted　according　to　the　temporal　correlation.　In　the　appearance　model,　we　define　the　position　factor　to　represent　the　different　importance　of　candidate　samples　in　different　positions　using　the　double　Gaussian　probability　model.　The　candidate　sample　with　highest　likelihood　is　selected　as　the　tracking　result　by　combining　the　holistic　and　local　responses　with　the　position　factor.　Moreover,　an　adaptive　template　updating　scheme　is　proposed　to　adapt　to　the　target＇s　appearance　changes,　especially　those　caused　by　fast　motion.　The　experimental　results　on　a　2013　benchmark　dataset　demonstrate　that　the　proposed　algorithm　performs　better　in　scenes　with　fast　motion　and　partial　or　full　occlusion　compared　to　the　state-of-the-art　algorithms.