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Open Access Foreground-Aware Statistical Models for Background Estimation

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Video-based detection of moving and foreground objects is a key computer vision task. Temporal differencing of video frames is often used to detect objects in motion, but fails to detect slowmoving (relative to the video frame rate) or stationary objects. Adaptive background estimation is an alternative to temporal frame differencing that relies on building and maintaining statistical models describing background pixel behavior; however, it requires careful tuning of a learning rate parameter that controls the rate at which the model is updated. We propose an algorithm for statistical background modeling that selectively updates the model based on the previously detected foreground. We demonstrate empirically that the proposed approach is less sensitive to the choice of learning rate, thus enabling support for an extended range of object motion speeds, and at the same time being able to quickly adapt to fast changes in the appearance of the scene.
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Keywords: Adaptive background models; Background estimation; Foreground detection; Motion detection; Statistical background models

Document Type: Research Article

Publication date: January 13, 2019

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

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