Rejecting False Positives in Video Object Segmentation

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.

A thorough performance evaluation of the employed features and classifiers is carried out in order to identify which visual cues/classifier allow for a better separability between true and false positives. The experimental results, obtained on three challenging datasets, showed that a post-processing layer exploiting the devised visual features is able a) to reduce the false alarm rate by about 10% to 20%, while keeping the number of true positives almost unaltered, and b) to generalize over different object classes and application domains.