The　tracking-by-detection　framework　receives　growing　attention　through　the　integration　with　the　convolutional　neural　networks　(CNNs).　Existing　tracking-by-detection-based　methods,　however,　fail　to　track　objects　with　severe　appearance　variations.　This　is　because　the　traditional　convolutional　operation　is　performed　on　fixed　grids,　and　thus　may　not　be　able　to　find　the　correct　response　while　the　object　is　changing　pose　or　under　varying　environmental　conditions.　In　this　paper,　we　propose　a　deformable　convolution　layer　to　enrich　the　target　appearance　representations　in　the　tracking-by-detection　framework.　We　aim　to　capture　the　target　appearance　variations　via　deformable　convolution,　which　adaptively　enhances　its　original　features.　In　addition,　we　also　propose　a　gated　fusion　scheme　to　control　how　the　variations　captured　by　the　deformable　convolution　affect　the　original　appearance.　The　enriched　feature　representation　through　deformable　convolution　facilitates　the　discrimination　of　the　CNN　classifier　on　the　target　object　and　background.　The　extensive　experiments　on　the　standard　benchmarks　show　that　the　proposed　tracker　performs　favorably　against　the　state-of-the-art　methods.