We improve the TLD algorithm and propose local and global search based on the sliding-window method

the Integral Histogram Filter

and Random Haar-like Feature Filter to solve the drift problem of traditional tracking algorithms in complex conditions. First

we use the Integral Histogram Filter to reject the Sliding-window patches as quickly as possible to release the feature matching in the following filters. Then

we use Random Haar-like Feature Filter to overcome the drift problem

which causes a loss of accuracy during the object tracking under complex conditions (multi-object

occlusion

fast movement). We ultimately combine filters of the TLD algorithm and two new filters of our proposed. The experimental results show that the proposed approaches compared with the traditional tracking algorithms not only presents robustness and tracking accuracy in stable background or complex conditions

but also obtains the best computing speed with the use of the local and global search. The proposed method is able to detect the multi-scale object accurately both in different environment and tracking object deformation. Combining the global and local search strategy can overcome the time consuming effectively to achieve the real-time object tracking.