This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex
scenes using a monocular, potentially moving, uncalibrated ca...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
Abstract. Object localization and tracking are key issues in the analysis of scenes for video surveillance or scene understanding applications. This paper presents a contribution t...
We present a discriminative model that casts appearance modeling and visual matching into a single objective for visual tracking. Most previous discriminative models for visual tra...
—We present an application of machine learning to the semi-automatic synthesis of robust servo-trackers for underwater robotics. In particular, we investigate an approach based o...