Abstract: Contour matching based textureless 3D object tracking method commonly use 3D-2D correspondence between the 3D object model and 2D object contour in the image to track the 3D object.However,this often fails in highly cluttered backgrounds or in presence of motion blur.To overcome this problem,we propose an 3D object tracking method based on adaptive feature fusion.Firstly,contour matching and local color statistics is performed nearby the projected contour of 3D model to extract contour feature and color feature.Then the energy function is defined based on adaptively weighted contour feature and statistical color feature,and the differentials of this energy function with respect to pose parameters of the 3D object are derived.Finally,the optimal pose is obtained via LM solver.To deal with fast motion of object and camera,a coarse-to-fine tracking strategy is applied iteratively on multi-scale video frames.Qualitative and quantitative experiments demonstrate that the proposed method improves robustness and accuracy with respect to cluttered backgrounds and motion blur effect.