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A Pixel Layering Framework For Robust Foreground Detection In Video

This work presents a framework for robust foreground detection that works under difficult conditions such as dynamic background and nominally moving camera. The proposed method includes two main components: coarse scene representation as the union of pixel layers, and foreground detection in video by propagating these layers using a maximum-likelihood assignment. Instead of modelling each pixel in the scene separately, we first cluster together pixels that share similar statistics. These pixels/samples are then used to create a non-parametric adaptive model of the cluster or layer. The entire scene is coarsely modelled as the union of such non-parametric layer-models. A pixel is then detected as foreground if it does not adhere to these adaptive models of the background. A principled way of computing detection thresholds is used to achieve robust detection performance with a pre-specified number of false alarms. Correlation between pixels in the spatial vicinity is exploited to deal with camera motion without precise registration. The proposed technique adapts to changes in the scene, and allows us to automatically convert persistent foreground objects to background and re-convert them to foreground when they become interesting. This simple framework addresses the important problem of robust foreground and unusual region detection, performed at about 10 frames per second on a standard laptop computer. The presentation of the proposed approach is complemented by results on challenging real data and comparisons with other standard techniques.