实现稳健的目标跟踪，建立有效的目标在线模型至关重要。针对现有在线学习跟踪算法缺乏对目标观测信息是否有效的判断，提出了一种简单且高效的解决方法。利用正负样本构建目标在线模型，基于压缩感知理论从多尺度图像特征空间提取特征信息完成目标表征之后，由随机蕨分类器进行分类并通过一种特征置信度度量策略来确定在线更新速率，最后由目标在线模型判断输出置信度最高的样本，此外还建立了一种遮挡反馈机制来决定是否更新目标在线模型。实验结果表明，该方法在目标被长时间遮挡、光照变化等情况下均能完成稳健跟踪，在320 pixel × 240 pixel 大小的视频序列中处理速度保持在30~50 frame/s左右，可以满足实时应用的需求。

It is crucial to establish an effective online model for robust tracking. As existing online learning tracking algorithms do not judge whether the objective observation information is effective, a simple and efficient solution is proposed. The positive and negative samples are applied to build online object model, then feature information is extracted from the multi-scale image feature space by compressive sensing to represent object, the random fern classifier is adopted to classify and determine the online update rate by a confidence measure strategy of features. The online object model will output the sample with the highest confidence, which is decided whether to update by an shelter feedback mecanism. Experimental results show that the proposed algorithm can complete the robust tracking under the condition of long-time occlusion, illumination changing, the video sequence of 320 pixel×240 pixel, the processing speed can keep 30~50 frame/s, which meets real-time application requirement.