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Understanding real-world scenes for human-like machine perception

Mustafa, Armin and Hilton, Adrian (2019) Understanding real-world scenes for human-like machine perception In: Machine Intelligence 21 (MI21-HLC) workshop, 30 Jun - 03 Jul 2019, Cumberland Lodge, Windsor, UK.

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The rise of autonomous machines in our day-to-day lives has led to an increasing demand for machine perception of real-world to be more robust, accurate and human-like. The research in visual scene un- derstanding over the past two decades has focused on machine perception in controlled environments such as indoor, static and rigid objects. There is a gap in literature for machine perception in general complex scenes (outdoor with multiple interacting people). The proposed research ad- dresses the limitations of existing methods by proposing an unsupervised framework to simultaneously model, semantically segment and estimate motion for general dynamic scenes captured from multiple view videos with a network of static or moving cameras. In this talk I will explain the proposed joint framework to understand general dynamic scenes for ma- chine perception; give a comprehensive performance evaluation against state-of-the-art techniques on challenging indoor and outdoor sequences; and demonstrate applications such as virtual, augmented, mixed reality (VR/AR/MR) and broadcast production (Free-view point video - FVV).

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 30 June 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
Uncontrolled Keywords : Scene Understanding; Reconstruction; Semantic Segmentation
Related URLs :
Depositing User : Clive Harris
Date Deposited : 18 Sep 2019 07:42
Last Modified : 18 Sep 2019 07:44

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