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首页> 外文期刊>Information Forensics and Security, IEEE Transactions on >Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking
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Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking

机译：鲁棒的视觉对象跟踪的联合特征对应和外观相似性

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摘要

A novel visual object tracking scheme is proposed by using joint point feature correspondences and object appearance similarity. For point feature-based tracking, we propose a candidate tracker that simultaneously exploits two separate sets of point feature correspondences in the foreground and in the surrounding background, where background features are exploited for the indication of occlusions. Feature points in these two sets are then dynamically maintained. For object appearance-based tracking, we propose a candidate tracker based on an enhanced anisotropic mean shift with a fully tunable (five degrees of freedom) bounding box that is partially guided by the above feature point tracker. Both candidate trackers contain a reinitialization process to reset the tracker in order to prevent accumulated tracking error propagation in frames. In addition, a novel online learning method is introduced to the enhanced mean shift-based candidate tracker. The reference object distribution is updated in each time interval if there is an indication of stable and reliable tracking without background interferences. By dynamically updating the reference object model, tracking is further improved by using a more accurate object appearance similarity measure. An optimal selection criterion is applied to the final tracker based on the results of these candidate trackers. Experiments have been conducted on several videos containing a range of complex scenarios. To evaluate the performance, the proposed scheme is further evaluated using three objective criteria, and compared with two existing trackers. All our results have shown that the proposed scheme is very robust and has yielded a marked improvement in terms of tracking drift, tightness, and accuracy of tracked bounding boxes, especially for complex video scenarios containing long-term partial occlusions or intersections, deformation, or background clutter with similar color distributions to the foreground object.
机译：通过结合点特征对应和物体外观相似度，提出了一种新颖的视觉目标跟踪方案。对于基于点特征的跟踪，我们提出了一种候选跟踪器，该跟踪器同时利用前景和周围背景中的两套单独的点特征对应关系，其中利用背景特征来指示遮挡。然后动态维护这两个集合中的特征点。对于基于对象外观的跟踪，我们提出了一种候选跟踪器，该跟踪器基于增强的各向异性均值偏移，具有完全可调的（五个自由度）边界框，该边界框部分由上述特征点跟踪器引导。两个候选跟踪器都包含一个重新初始化过程，以重置跟踪器，以防止累积的跟踪错误在帧中传播。此外，一种新颖的在线学习方法被引入到基于均值漂移的增强型候选跟踪器中。如果有稳定且可靠的跟踪指示而没有背景干扰，则在每个时间间隔内都会更新参考对象分布。通过动态更新参考对象模型，可以通过使用更准确的对象外观相似性度量来进一步改善跟踪。根据这些候选跟踪器的结果，将最佳选择标准应用于最终跟踪器。已经对包含一系列复杂场景的多个视频进行了实验。为了评估性能，使用三个客观标准对提议的方案进行了进一步评估，并与两个现有的跟踪器进行了比较。我们所有的结果都表明，所提出的方案非常健壮，并且在跟踪漂移，紧密度和跟踪边界框的准确性方面均取得了显着改善，尤其是对于包含长期局部遮挡或相交，变形或具有与前景对象相似的颜色分布的背景杂波。

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