Showing papers by "David G. Lowe published in 2013"

Self-Learning for Player Localization in Sports Video

Abstract: This paper introduces a novel self-learning framework that automates the label acquisition process for improving models for detecting players in broadcast footage of sports games. Unlike most previous self-learning approaches for improving appearance-based object detectors from videos, we allow an unknown, unconstrained number of target objects in a more generalized video sequence with non-static camera views. Our self-learning approach uses a latent SVM learning algorithm and deformable part models to represent the shape and colour information of players, constraining their motions, and learns the colour of the playing field by a gentle Adaboost algorithm. We combine those image cues and discover additional labels automatically from unlabelled data. In our experiments, our approach exploits both labelled and unlabelled data in sparsely labelled videos of sports games, providing a mean performance improvement of over 20% in the average precision for detecting sports players and improved tracking, when videos contain very few labelled images.

Methods and apparatus for generating composite images

Abstract: Apparatuses and methods for capturing and generating composite images. In various aspects, the invention provides apparatuses and methods for correcting position information of captured images received by position sensors based on alignment of overlapping images. Corrected position information is then taken into account when displaying the locations of captured images on a display for providing guidance to users for generating composite images.