In order to solve the problem of accurate tracking and scale estimation in videos where targets change their scales, we propose a scale adapted tracking algorithm based on kernelized correlation. Firstly, we establish kernel ridge regression model and construct a two-dimensional kernelized correlation location filter. The center location of target is determined precisely by using fused multi-channel features. Then, the multi-scale samples of target area are obtained and their sizes are reset to the same with the model. By extracting their features and reconstructing to one-dimensional vector, we construct the one-dimensional kernelized scale filter to achieve optimal scale estimation. The experimental results on OTB2013 platform, especially on the scale changing benchmark dataset indicate that the proposed algorithm performs better in precision and success rate in comparison with eight mainstream tracking algorithms. Meanwhile, this algorithm can not only achieve an adapted tracking to the scale changing of target, but also locate its position fast and effectively.