This paper proposes the saliency-based feature fusion and template updating strategy in the meanshift framework to realize robust tracking in dynamic complex environment. Firstly, A feature saliency measurement is defined as the contrast between object and background. The optimal color space based on feature saliency and the texture feature based on Gabor wavelet sparse coding are extracted. The reference histogram template initialization is weighted by feature saliency and an online template updating strategy is presented for occlusion and object deformation. Experimental results show that the proposed model is more precise and more robust compared with up-to-date competing models.