The team | GreenAiUPPA
We are investigating the problem of speaker and face identification in broadcast videos. Identification is performed by associating automatically extracted names from overlaid texts with speaker and face clusters. We aimed at exploiting the structure of news videos to solve name/cluster association ambiguities and clustering errors. The proposed approach combines iteratively two conditional random fields (CRF). The first CRF performs the person diarization (joint temporal segmentation, clustering, and assoc
This paper proposes a probabilistic approach to recover affine camera calibration and objects position/occupancy from multi-view images using solely the information from image detections. We show that remarkable object localisation and volumetric occupancy can be recovered by including both geometrical constraints and prior information given by objects CAD models from the ShapeNet dataset. This can be done by recasting the problem in the context of a probabilistic framework based on PPCA that enforces both
This paper presents an efficient framework to include the information of objects position in classical multi-view geometry problems for 3D reconstruction. In particular, we present two main contributions to Structure from Motion (SfM) using factorization methods for the affine camera case. First, we introduce a method based on factorization that extends the classical 3D point cloud reconstruction based on 2D point correspondences to objects using detection correspondences. In this case, objects are approxim
The team | GreenAiUPPA