Traditional video scene analysis depends on accurate background modeling to identify salient foreground objects. However, in many important surveillance applications, saliency is defined by the appearance of a new non-ephemeral object that is between the foreground and background. This midground realm is defined by a temporal window following the object's appearance; but it also depends on adaptive background modeling to allow detection with scene variations (e.g., occlusion, small illumination changes). The human visual system is ill-suited for midground detection. For example, when surveying a busy airline terminal, it is difficult (but important) to detect an unattended bag which appears in the scene. This paper introduces a midground detection technique which emphasizes computational and storage efficiency. The approach uses a new adaptive, pixel-level modeling technique derived from existing backgrounding methods. Experimental results demonstrate that this technique can accurately and efficiently identify midground objects in real-world scenes, including PETS2006 and AVSS2007 challenge datasets.
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