A new on-line boosting algorithm based on sub-regional classifiers was presented to solve the problem that traditional on-line boosting based tracking algorithm often leads to drifting or failure due to the accumulated error during on-line updating under serious occlusions. Firstly, the feature pool was divided into a number of sub-regional feature pools to correspond to their sub-regional classifiers. Then, the sub-regional classifiers were selected adaptively into a strong classifier to eliminate the influence of occluded sub-regions on the target location when occlusions took place. Finally, the sub-regional feature pools were updated partly to solve the problem of accumulated error during on-line learning. The proposed algorithm was tested with variant video sequences and results show that proposed algorithm achieves exact tracking for the object occluded, and the average computing frame rate is 15 frame/s when the object scale is 36 pixel×40 pixel. In conclusion,the algorithm can satisfy the requirements of stability under occlusion as compared with the original on-line boosting algorithm.