A One-Threshold Algorithm for Detecting Abandoned Packages Under Severe Occlusions Using a Single Camera

TitleA One-Threshold Algorithm for Detecting Abandoned Packages Under Severe Occlusions Using a Single Camera
Publication TypeJournal Articles
Year of Publication2006
AuthorsLim S-N, Davis LS
JournalTechnical Reports from UMIACS
Date Published2006/02/13/T21:4
KeywordsTechnical Report

We describe a single-camera system capable of detecting abandoned packages under severe occlusions, which leads to complications on several levels. The first arises when frames containing only background pixels are unavailable for initializing the background model - a problem for which we apply a novel discriminative measure. The proposed measure is essentially the probability of observing a particular pixel value, conditioned on the probability that no motion is detected, with the pdf on which the latter is based being estimated as a zero-mean and unimodal Gaussian distribution from observing the difference values between successive frames. We will show that such a measure is a powerful discriminant even under severe occlusions, and can deal robustly with the foreground aperture effect - a problem inherently caused by differencing successive frames. The detection of abandoned packages then follows at both the pixel and region level. At the pixel-level, an ``abandoned pixel'' is detected as a foreground pixel, at which no motion is observed. At the region-level, abandoned pixels are ascertained in a Markov Random Field (MRF), after which they are clustered. These clusters are only finally classified as abandoned packages, if they display temporal persistency in their size, shape, position and color properties, which is determined using conditional probabilities of these attributes. The algorithm is also carefully designed to avoid any thresholding, which is the pitfall of many vision systems, and which significantly improves the robustness of our system. Experimental results from real-life train station sequences demonstrate the robustness and applicability of our algorithm.