目标模型更新中存在的模型漂移问题,是影响视频跟踪结果的一个重要因素。针对这一难题,提出了一种新的基于前景分割的目标跟踪算法。算法通过引入条件随机场(CRF)模型对跟踪区域和非跟踪区域的时空关系进行建模,实现对图像序列中像素点的标记,标记为跟踪目标或背景,并使用在线学习方法,根据场景的变化调整CRF模型的参数。跟踪过程中,通过对CRF模型的求解,得到最优的标记场和目标像素的置信图像;利用置信图像,结合目标模型的相似性度量定位整个目标;根据目标区域内的标记结果,使用一种选择性采样的方式更新目标模型,从而解决更新中的漂移问题。通过在多个典型的复杂场景中进行实验,验证了该算法的有效性。

Target model drift is an important factor for visual tracking. To deal with the problem,a novel adaptive tracking algorithm is proposed. The spatial-temporal constraints of the tracking region and non-tracking region in sequence are modeled through a conditional random field (CRF),the parameters of which are updated using online learning method according to the changes of scene. The pixels of all images can be labeled as target or background based on the CRF model. During tracking,the optimal label field and the confidence map are first achieved by resolving the CRF model. Then the similarity measure between the target model and the target candidates,combined with the confidence map,is fed to the mean shift algorithm for the target localization. A selective sampling update strategy is utilized to alleviate the model drift. The experimental results demonstrate the efficiencies of the proposed algorithm in several real sequence testings.