6533b863fe1ef96bd12c77b9
RESEARCH PRODUCT
Midground Object Detection in Real World Video Scenes,
Brian ValentineSenyo ApewokinLinda M. WillsScott WillsAntonio Gentilesubject
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScene statisticsObject (computer science)Object detectionObject-class detectionComputational efficiencyComputer networksSalientVideo trackingHuman visual system modelComputer visionViola–Jones object detection frameworkArtificial intelligencebusinessdescription
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 A VSS2007 challenge datasets.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-09-01 |