Visual　object　tracking　is　a　core　technology　and　challenging　research　in　computer　vision.　With　the　rapid　development　of　unmanned　aerial　vehicle　(UAV),　more　and　more　UAVs　are　equipped　with　video　cameras　to　conduct　object　tracking.　Researching　object　tracking　of　UAV　image　sequences　is　of　great　help　for　practical　applications.　Recent　trackers　based　on　deep　networks　have　a　great　success　by　learning　an　effective　representation　of　targets,　however,　the　offline　training　is　time-consuming,　and　the　complex　network　may　reduce　its　efficiency.　In　this　paper,　to　effectively　decrease　the　training　time　and　handle　the　problem　of　network　complexity,　an　effective　tracking　algorithm　called　multi-scale　gcForest　tracking　(MSGCF)　is　proposed.　First,　to　enrich　the　sample　information　and　overcome　the　problem　of　scale　variations,　the　multi-scale　transformation　is　used　for　helping　network　to　obtain　the　stronger　representation　of　target　samples.　Then,　gcForest　that　is　a　decision　tree　ensemble　approach　is　utilized　in　tracking　task　to　extract　the　target　features,　and　gcForest　is　much　easier　to　train　and　works　well　in　small-scale　training　data.　Most　importantly,　we　make　improvements　to　the　gcForest　by　adding　three　channels　at　the　top　of　network　to　adapt　to　multi-scale　images　and　improve　the　representation　ability　of　network.　Furthermore,　computation　speed　can　be　increased　greatly　through　the　method　of　reducing　feature　dimension　based　on　compressive　sensing　(CS)　theory,　and　the　power　of　features　can　also　be　reserved.　Finally,　a　support　vector　machine　(SVM)　classifier　is　employed　to　effectively　separate　targets　and　backgrounds.　Extensive　experimental　results　on　UAV　image　sequences　and　challenging　benchmark　data　sets　demonstrate　that　the　proposed　MSGCF　algorithm　is　effective.