压缩跟踪算法作为一种新的算法, 具有简单、高效、实时的优点, 但该算法依然存在缺陷。首先, 在复杂背景或有遮挡等情况下, 容易较快的引进误差; 其次, 跟踪窗口保持不变, 使得不能正确跟踪目标位置且不能准确更新正负样本; 最后, 搜索样本数目大, 导致跟踪速度不理想。针对这些问题, 利用前后帧跟踪点的直方图对比来判断遮挡的发生, 并自适应的改变更新系数; 采用在原算法最优匹配点周围小范围多尺度搜索更优位置的方法, 来适应目标尺寸的变化; 引入粗精跟踪策略, 在不同阶段使用不同数量的子特征集进行匹配, 以筛选样本、减少计算量。这些改进避免了算法缺陷导致的跟踪失败, 提高了跟踪效率。实验证明, 改进后的算法比原算法具有更好的鲁棒性且跟踪速度更快。

Real-time compressive tracking was a simple and effective tracking algorithm. However, there were a number of problems which need to be addressed. First of all, it was easy to introduce errors due to factors such as occlusion and clutter. Secondly, it couldn’t update the positive and negative samples accurately while using fixed tracking window. At last, the number of testing samples was too large, which affected the speed of tracking. The occlusion was checked by comparison between consecutive frames’ histograms, and the coefficient can be also updated adaptively by the comparison result. We searched for more specified areas with multi-scales to find out the best matching place, and to handle scale change of the target on the basis of the original algorithm’s tracking result. The different numbers of sub features sets were utilized to filter the testing samples. In that case, the speed of tracking process would be improved. The strategies we proposed would improve the original algorithm’s performance to avoid the failure of tracking. The experimental results indicate that the algorithm can run in real-time and perform favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.