Understanding real-world scenes for human-like machine perception
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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).
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[2] Marc Pollefeys,et al. Unstructured video-based rendering: interactive exploration of casually captured videos , 2010, SIGGRAPH 2010.
[3] Jean-Yves Guillemaut,et al. 4D Temporally Coherent Light-Field Video , 2017, 2017 International Conference on 3D Vision (3DV).
[4] Adrian Hilton,et al. Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).