Vehicle　tracking　is　one　of　the　most　challenging　tasks　in　the　field　of　visual　tracking.　A　vehicle　tracking　algorithm　based　on　CNN　is　constructed　to　solve　the　problem　of　rapid　movement,　scale　change　and　occlusion　of　vehicles　in　outdoor　environment.　The　CNN　is　used　to　extract　feature　sets　containing　positive　and　negative　samples.　The　output　of　the　CNN　is　used　as　the　input　of　the　Logistics　classifier　to　obtain　the　vehicle　classifier,　and　the　particle　filter　is　used　to　track　the　target　online.　The　experimental　results　show　that　the　depth　characteristics　of　CNN　extraction　can　effectively　distinguish　between　the　target　and　the　background,　and　combined　with　particle　filtering　algorithm　for　online　tracking,　it　has　high　tracking　accuracy　and　strong　robustness.　Compared　with　the　existing　tracking　algorithms,　the　vehicle　can　be　better　tracked　when　faced　with　changes　in　lighting,　vehicle　occlusion,　and　scale　changes.