An improved spatio

Spatio-temporal context algorithm is commonly used in geology and has been introduced into the field of target tracking in recent years. The algorithm improved the robustness of visual tracking through dense contextual information around the target and thus achieved great tracking results. However, updating errors may occur in spatio-temporal context algorithm when the target has rapid changes in scale and appearance, resulting in the algorithm cannot extract the target area accurately and completely. In order to overcome this problem, this article proposes an improved spatio-temporal context algorithm based on scale correlation filter. First of all, the algorithm extracts samples of different scales around the target after the target is settled by spatio-temporal context algorithm and then forms the pyramid of scale characteristics through samples extracted by histogram of oriented gradients operator. Second, the best scale parameter will be achieved by means of scale correlation filter to update the scale model. Finally, the results show that the algorithm has good tracking effect and robustness through two experiments which are contrast experiment before and after the improvement of algorithm and comparison experiment with other advanced algorithms