As the accuracy of moving object tracking in video surveillance is disturbed by occlusion, camera moving and target appearance changing, an algorithm based on piecewise fusion weight and multi-strategy was proposed. Firstly, the piecewise fusion weight was constructed by combining the feature of object, background and candidate regions to obtain the likelihood image of object location. Then, the likelihood image was segmented with the spatial coherent and hysteresis threshold to suppress noise interference. Meanwhile, the object occlusion was determined and handled by analyzing the change of the piecewise fusion weight and enlarging the candidate area. Furthermore, the object scale was adaptively adjusted according to history and current scales. Finally, object information and background regions were dynamically updated to adapt to the object appearance and scene changing. Experimental results compared with other traditional methods show that the proposed algorithm is applicable to process the moving object tracking in low-contrast scenes in real time, and the average processing time for different video images is 10 ms, which means that the algorithm is suitable for the moving object tracking in complex scenes.