Tracking Persons-of-Interest via Unsupervised Representation Adaptation

Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically
different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up. Existing multi-target tracking methods often use low-level features which are not sufficiently discriminative for identifying faces with such large appearance variations. Read More “Tracking Persons-of-Interest via Unsupervised Representation Adaptation”

Unsupervised 3D Pose Estimation for Hierarchical Dance Video

Dance experts often view dance as a hierarchy of information, spanning low-level (raw images, image sequences), mid-levels (human poses and bodypart movements), and high-level (dance genre). We propose a Hierarchical Dance Video Recognition framework (HDVR). HDVR estimates 2D pose sequences, tracks dancers, and then simultaneously estimates corresponding 3D poses and 3D-to-2D imaging parameters, without requiring ground truth for 3D poses. Read More “Unsupervised 3D Pose Estimation for Hierarchical Dance Video”

Connected Segmentation Tree For Object Modeling

We propose a new object representation, called connected segmentation tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the object’s segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. Read More “Connected Segmentation Tree For Object Modeling”