Sascha Schreiber

Learn More
This paper encompasses the analysis of meetings for a seg-mentation into sub-genres. Therefore an approach on a higher semantic level has been chosen. The algorithms make use of the results of specialized recognizers like a speaker turn detector and a gesture recognizer. Basically, the goal of this investigation was to answer the question, how well meeting(More)
The project Augmented Multi-party Interaction (AMI) is concerned with the development of meeting browsers and remote meeting assistants for instrumented meeting rooms – and the required component technologies R&D themes: group dynamics, audio, visual, and multimodal processing, content abstraction, and human-computer interaction. The audiovisual processing(More)
This paper deals with the fully automatic extraction of clas-sifiable person features out of a video stream with challenging background. Basically the task can be split in two parts: Tracking the object and extracting distinctive features. In order to track a person, a system composed of an Active Shape Model embedded in a particle filter framework has been(More)
In this papers 4 , we present the findings of the Augmented Multiparty Interaction (AMI) project investigation on the localization and tracking of 2D head positions in meetings. The focus of the study was to test and evaluate various multi-person tracking methods developed in the project using a standardized data set and evaluation methodology.
In this paper a view-independent head tracking system applying an Active Shape Model based particle filter is used to find precise image sections. DCTmod2 feature sequences are extracted from these sections and given as input to Cyclic Pseudo two-dimensional Hidden Markov Model based classi-fiers. These classifiers are trained to recognize the identity of(More)
In this paper, we present the findings of the Augmented Mul-tiparty Interaction (AMI) project investigation on the localization and tracking of 2D head positions in meetings. The focus of the study was to test and evaluate various multi-person tracking methods developed in the project using a standardized data set and evaluation methodology.
  • 1