This paper presents an approach to classify static foreground blobs in surveillance scenarios. Possible application is\nthe detection of abandoned and removed objects. In order to classify the blobs, we developed two novel features\nbased on the assumption that the neighborhood of a removed object is fairly continuous. In other words, there is a\ncontinuity, in the input frame, ranging from inside the corresponding blob contour to its surrounding region.\nConversely, it is usual to find a discontinuity, i.e., edges, surrounding an abandoned object. We combined the two\nfeatures to provide a reliable classification. In the first feature, we use several local histograms as a measure of similarity\ninstead of previous attempts that used a single one. In the second, we developed an innovative method to quantify\nthe ratio of the blob contour that corresponds to actual edges in the input image. A representative set of experiments\nshows that the proposed approach can outperform other equivalent techniques published recently.
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