DUAL INFORMATION-BASED BACKGROUND MODEL FOR MOVING OBJECT DETECTION

In this article, a novel pixel based object detection framework is proposed that leverages dual type pixel-level information to construct the background model. The first type of information is initially used intensity histograms over a training set of a few initial video frames. Finally, it is formed by gathering all the minimum and maximum values of contiguous non-zero frequencies of the temporal intensity histogram. The second type of information constitutes a set having only the discrete pixel values. Subsequently, a pixel-level periodic updating scheme is used to make the model robust and flexible enough to recognize and detect foregrounds in various critical background environments. This dual format model produces effective results over many state-of-the-art methods in a large variety of challenging real-life video sequences.