There are problems in traditional compressive tracking algorithms of few extracted features, high sensitivity to illumination changes and inadaptability to the changes in target scale. To solve these problems, an adaptive-scale compressive tracking algorithm fusing HOG-like features is proposed.On the basis of Haar-like features, the new algorithm uses the method of fixed ratio to fuse HOG-like features, so as to reduce its sensitivity to illumination. The integral graph algorithm is used to accelerate the calculation of HOG-like features.In addition, a scale estimation method based on correlation filter is proposed, so as to find out the scale to which the scale filter has the biggest response and then take it as the new target scale.The size of the tracking window is timely adjusted and the feature extraction template is updated.Therefore, the problem of scale change is solved. The experimental results show that the improved algorithm can adapt to marked illumination changes, scale changes and in-plane rotation.Its tracking accuracy and robustness are improved greatly, which satisfies the requirements of real-time tracking.