针对增强现实技术在移动智能设备上应用需求，设计了一种适应移动智能设备嵌入式系统的目标跟踪器。在特征描绘阶段采用亮度信息进行二值特征的快速分割并建立强显著性的二值特征描绘器；在特征选择阶段提出一种稀疏跟踪搜索模板进一步提高跟踪算法的执行效率。然后，在跟踪器中建立了存储目标初始信息的静态库与不断更新目标外观或运动变化信息的动态库，通过对比静、动态库与搜索模板区域中的信息确立跟踪目标位置。对跟踪器执行时间进行了对比， 结果表明：在保证较高的跟踪精度条件下，采用稀疏搜索模板能明显改善算法的执行时间，将搜索半径设为10可在绝大多数情况下满足跟踪器的实时要求。对跟踪器的有效能力亦进行了对比，结果显示：在3组不同搜索半径下，提出的DBRISK描绘方法的平均跟踪误差相对于BRISK（Binary Robust Invariant Scalable Keypoints）方法分别下降了16%、28%和29%。实验表明：提出的方法能够明显改善跟踪器的信息提取准确度，适用于计算能力有限的移动智能设备。

To apply augmented reality technology in mobile smart devices, a novel target tracker for the embedded system of a smart mobile device was proposed. In the stage of feature description, the binary feature was segmented rapidly and discriminative binary descriptors were established by fast brightness segment. In the stage of feature matching, a sparse searching template was proposed to improve the execution efficiency of the tracking algorithm. Initial target information was stored in a static library established by the tracker and the change of target appearance was stored in a dynamic library, and the target location was tracked by comparing search information and the templates in libraries. The execution time of the tracker was compared, and the results show that sparse search templates significantly improve the execution time of the algorithm at maintaining a higher tracking accuracy, and setting the search radius to 10 is able to meet the real-time requirements of trackers. The effective capacity of the tracker was compared also. And the results indicate that the tracking error of DBRISK are 16%, 28% and 29% decrease than those of the original BRISK (Binary Robust Invariant Scalable Keypoints) under three different search radii, which means the proposed method significantly improves the accuracy of tracking method, and is applicable to the limited computing power smart mobile devices.