Robust Abandoned Object Detection Using Dual Foregrounds

Abstract

As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate long- and short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.

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