Patch-based local histograms and contour estimation for static foreground classification

Abstract

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

A Comprehensive Survey of Video Datasets for Background Subtraction

“…As for ground truth information, pixel-wise segmented foreground masks are provided for the objective evaluation of motion-based video segmentation algorithms. In [88] and [89], cVSG dataset has been used by researchers for foreground detection in video sequences.…”