Scene-centric Joint Parsing of Cross-view Videos
This work addresses an under-explored problem in computer vision for researchers and practitioners, but it appears incremental as it builds on existing view-centric methods.
The paper tackles cross-view video understanding by introducing a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs, resulting in improved predictions as shown in quantitative experiments.
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.