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首页> 外文会议>Visual Communications and Image Processing 2007 pt.2; Proceedings of SPIE-The International Society for Optical Engineering; vol.6508 pt.2; Electronic Imaging Science and Technology >Occlusion and split detection and correction for object tracking in surveillance applications
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Occlusion and split detection and correction for object tracking in surveillance applications

机译：遮挡，分割检测和校正，用于监视应用中的对象跟踪

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摘要

This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in feature-based tracking of objects for surveillance applications. The proposed algorithm detects sudden variations of spatio-temporal features of objects in order to identify possible occlusion or split events. The detection is followed by a validation stage that uses past tracking information to prevent false detection of occlusion or split. Special care is taken in case of heavy occlusion, when there is a large superposition of objects. In this case the system relies on long-term temporal behavior of objects to avoid updating the video object features with unreliable (e.g. shape and motion) information. Occlusion is corrected by separating occluded objects. For the detection of splits, in addition to the analysis of spatio-temporal changes in objects features, our algorithm analyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects, such as in the deposit of an object. Split is corrected by physically merging the objects detected to be split. To validate the proposed approach, objective and visual results are presented. Experimental results show the ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposed algorithm is most suitable in video surveillance applications due to: its good performance in multiple, heavy, and total occlusion; its distinction between real object separation and faulty object split; its handling of simultaneous occlusion and split events; and its low computational complexity.
机译：本文提出了一种新的算法，用于监视应用中基于特征的目标跟踪中的遮挡和分裂的实时检测和校正。所提出的算法检测对象的时空特征的突然变化，以便识别可能的遮挡或分裂事件。在检测之后是验证阶段，该阶段使用过去的跟踪信息来防止错误检测阻塞或分裂。当物体大量重叠时，要特别注意重度咬合。在这种情况下，系统依赖于对象的长期时间行为，以避免用不可靠的（例如形状和运动）信息来更新视频对象特征。通过分离被遮挡的物体来校正遮挡。为了检测分裂，除了分析对象特征的时空变化外，我们的算法还分析了分裂对象的时间行为，以区分对象的分割和实际分离中的错误，例如对象的沉积。通过物理合并检测到要拆分的对象来校正拆分。为了验证所提出的方法，提出了客观和视觉结果。实验结果表明，该算法具有检测和校正物体分裂和遮挡的能力。所提出的算法最适合视频监控应用，原因是：在多次，重度和完全遮挡下性能良好；区分真实对象和错误对象处理同时发生的闭塞和分裂事件；其计算复杂度低。

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