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Li Z. Vision-based manipulative gesture recognition in a human-robot interaction scenario . Bielefeld (Germany): Bielefeld University; 2008. ; Nowadays, many people are expecting an age of personal robots just as what happened in the evolution of computers. With this background, the research on human-robot interaction receives a lot of attention in the robotics research community. In this dissertation, we focus on the vision-based recognition of human's manipulative gestures because the manipulation of objects draws the attention of the communication partner on the objects that are relevant for a performed task and furthermore the recognition of them serves the goal of a more pro-active behavior of the robot in passive, more observational situations. Comparing to the interpretation of communicative gestures, which can be recognized purely based on trajectory information, the understanding of manipulative gestures is more relying on the object contexts. Different to others, the approach we propose is called object-oriented w.r.t. three different aspects: it is object-centered in terms of trajectory features that are defined relative to an object, it uses object-specific models for action primitives, and it has an object-attention mechanism which is based on task models. While most of the related work in gesture recognition assumes a fixed static camera view, such kind of constraints do not apply for mobile robot companions. After an analysis of the observational scenario, a 2-D approach was chosen by us. The manipulative primitive recognition scheme is able to generalize a primitive model, which has been learned from data items observed from a single camera view, to variant view points and different settings. We tackle the problem of compensating the view dependence of 2-D motion models on three different levels. Firstly, the trajectories are pre-segmented based on an object vicinity that depends on the camera tilt and object detections. Secondly, an interactive feature vector is designed to represent the ...