To　solve　the　problem　that　the　existing　video　object　tracking　(VOT)　methods　based　on　siamese　networks　have　poor　tracking　results　due　to　the　lack　of　feature　extraction　ability,　such　as　severe　object　appearance　change　and　out-of-plane　rotation,　a　VOT　method　based　on　residual　dense　siamese　networks　was　proposed.　First,　the　residual　dense　network　embedded　convolutional　block　attention　module　was　designed　to　extract　features　at　different　levels　from　the　template　image　and　the　detection　image.　Then,　the　features　of　different　levels　were　interlinked　by　an　independent　region　proposal　network.　Finally,　the　outputs　of　multiple　region　proposal　networks　were　summed　up　adaptively　and　the　final　tracking　result　was　obtained.　Results　show　that　the　proposed　method　can　achieve　better　tracking　effect　when　dealing　with　challenges　such　as　severe　object　appearance　change　and　out-of-plane　rotation.　©　2022,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.