Abstract
False-positive removal is a necessary step for robust video object segmentation because of the presence of visual noise introduced by unavoidable factors such as background movements, light changes, artifacts, etc. In this paper we present a set of generic visual cues that enable the discrimination between true positives and false positives detected by a video object segmentation approach. The devised object features encode real-world object properties, such as shape regularity, marked boundaries, color and texture uniformity and motion continuity and can be used in a post-processing layer to reject false positives.
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