Siamese　networks　have　been　paid　more　attention　to　video　tracking　due　to　its　superiority　in　balance　accuracy　and　speed.　Based　on　the　convolutional　feature　cross-correlation　between　the　target　template　and　the　search　region,　trackers　with　Siamese　network　can　search　for　the　best　result　in　the　candidate　box　to　get　the　tracking　result.　However,　existing　Siamese　tracking　algorithms　are　often　affected　by　motion　blurring,　low　resolution,　distortion　and　other　issues　that　blur　search　region　in　solving　video　object　tracking　problems.　This　paper　presents　a　candidate　box　area　generation　method　based　on　kernel　density　function　to　relocate　the　search　region　when　track　failed.　Specifically,　the　tracker　proposed　in　this　paper　fuses　deep　feature　and　color　feature　to　generate　candidate　boxes　from　which　more　accurate　tracking　results　can　be　obtained,　moreover,　the　color　feature　is　easily　to　calculate　to　reach　real-time　speed.　Finally,　by　improving　the　candidate　box　generation　algorithm,　the　problem　of　tracking　missing　due　to　fast　motion,　blurring　and　other　factors　is　effectively　solved　with　less　time　consuming.　©　2020　ACM.