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- W2067140628 abstract "This paper proposes a novel algorithm for the real-time detection and correction of occlusion and split in feature-basedtracking of objects for surveillance applications. The proposed algorithm detects sudden variations ofspatio-temporal features of objects in order to identify possible occlusion or split events. The detection isfollowed 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 casethe system relies on long-term temporal behavior of objects to avoid updating the video object features withunreliable (e.g. shape and motion) information. Occlusion is corrected by separating occluded objects. Forthe detection of splits, in addition to the analysis of spatio-temporal changes in objects features, our algorithmanalyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separationof objects, such as in the deposit of an object. Split is corrected by physically merging the objects detected to besplit. To validate the proposed approach, objective and visual results are presented. Experimental results showthe ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposedalgorithm is most suitable in video surveillance applications due to: its good performance in multiple, heavy, andtotal occlusion; its distinction between real object separation and faulty object split; its handling of simultaneousocclusion and split events; and its low computational complexity." @default.
- W2067140628 created "2016-06-24" @default.
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- W2067140628 date "2007-01-28" @default.
- W2067140628 modified "2023-09-23" @default.
- W2067140628 title "Occlusion and split detection and correction for object tracking in surveillance applications" @default.
- W2067140628 doi "https://doi.org/10.1117/12.704537" @default.
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