The　occlusion　in　traffic　scenarios　have　posed　great　challenges　in　vehicle　tracking.　Establishing　effective　and　robust　vehicle　tracking　algorithms　under　occlusion　conditions　is　a　necessary　issue　for　many　traffic　applications.　This　paper　presents　a　novel　approach　of　vehicle　tracking　by　fusing　the　prior　information　of　Kalman　filter.　The　prior　information　of　Kalman　filter　is　used　for　background　update,　precise　morphological　operation　and　occlusion　judgment.　The　fusion　of　observation　and　prior　information　is　adopted　to　segment　vehicles　under　occlusion.　It　effectively　improves　the　robustness　of　the　vehicle　tracking　algorithm　in　the　case　of　occlusion.　A　novel　vehicle　description　method　is　proposed　to　express　vehicle　shape　more　accurately　and　thus　reduces　the　misjudgement　of　occlusion.　Experiments　show　that　the　proposed　algorithm　can　accurately　track　vehicles　even　under　occlusion.