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We propose an efficient 3D upper body tracking method, which recovers the positions and orientations of six upper-body parts from the video sequence. Our method is based on a probabilistic graphical model (PGM), which incorporates the spatial relationships among the body parts, and a robust multi-view image likelihood using probabilistic PCA (PPCA). For the efficiency, we use a tree-structured graphical...
This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled...
We propose a novel method for removing irrelevant frames from a video given user-provided frame-level labeling for a very small number of frames. We first hypothesize a number of windows which possibly contain the object of interest, and then determine which window(s) truly contain the object of interest. Our method enjoys several favorable properties. First, compared to approaches where a single...
Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples from human body images. In this paper, a DTG-based graphical model is studied for human-body landmark localization and tracking along the image sequence. Experimental results on human motion databases are shown.
Building face models is an essential task in face recognition, tracking and etc. However, most of the current techniques require hand-labelling or special machinery such as cyber-scanner to extract the face model. In the paper, we propose an unsupervised algorithm to learn the face texture from video. The proposed approach models the video sequence as a mixture of dynamic face-layers and background...
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