Hand　tracking　is　a　crucial　step　in　vision　based　gesture　interaction.　Due　to　illumination　and　hand　shape　variation,　tracking　of　such　object　is　a　difficult　task.　In　this　paper,　an　ensemble　tracking　framework　integrated　with　superpixels　for　hand　tracking　is　proposed.　With　treating　tracking　as　a　binary　classification　problem,　ensemble　tracking　utilizes　weak　classifiers　to　get　strong　classifier　and　get　robustness　to　illumination　and　appearance　change.　However,　outliers　in　confidence　map　caused　by　irregular　object　shape　bring　ill　effects　to　tracking　performance.　As　hand　area　has　similar　color　and　texture,　superpixel　method　can　be　used　to　cluster　the　pixels　in　hand　area　and　provide　solution　to　exclude　outliers.　Experiments　show　that　with　superpixe　integrated,　the　proposed　method　get　more　accurate　tracking　result.　©　Springer　International　Publishing　Switzerland　2013.