We propose a novel approach for multi-person trackingby-
detection in a particle filtering framework. In addition
to final high-confidence detections, our algorithm uses the
con...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
Graph-based semi-supervised learning has gained considerable
interests in the past several years thanks to its effectiveness
in combining labeled and unlabeled data through
labe...
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
In this paper, a motion-based approach for detecting high-level semantic events in video sequences is presented. Its main characteristic is its generic nature, i.e. it can be dire...
This paper presents a resource typing framework for the Guru d-programming language, in which abstractions for various kinds of program resources can be defined. Implemented exam...