  Cross-view video understanding is an important yet under-explored area in
computer vision. In this paper, we introduce a joint parsing framework that
integrates view-centric proposals into scene-centric parse graphs that
represent a coherent scene-centric understanding of cross-view scenes. Our key
observations are that overlapping fields of views embed rich appearance and
geometry correlations and that knowledge fragments corresponding to individual
vision tasks are governed by consistency constraints available in commonsense
knowledge. The proposed joint parsing framework represents such correlations
and constraints explicitly and generates semantic scene-centric parse graphs.
Quantitative experiments show that scene-centric predictions in the parse graph
outperform view-centric predictions.
