Because a people detection system that considers only a single feature tends to be unstable, many people detection systems have been proposed to extract multiple features simultaneously. These detection systems usually integrate features using a heuristic method based on the designers' observations and induction. Whenever the number of features to be considered is changed, the designer must change and adjust the integration mechanism accordingly. To avoid this tedious process, we propose a multimodal fusion system that can detect and track people in a scalable, accurate, robust, and flexible manner. Each module considers a single feature and all modules operate independently at the same time. A depth module is constructed to detect people based on the depth-fromstereo method, and a novel approach is proposed to extract people by analyzing the vertical projection in each layer. A color module that detects the human face, and a motion module that detects human movement are also developed. The outputs from these individual modules are fused together and tracked over time, using a Kalman filter. V
The traditional e-learning systems are generally less attractive to students due to their lack of 3-D immersion and real voice interaction. The technology of virtual reality can be exploited to compensate these weaknesses. We propose a realistic and interactive virtual English classroom entitled VEC3D by integrating vivid 3-D graphics and real-time voice communication. The goal of VEC3D aims to help undergraduate students develop the overall English communicative competence in listening, speaking, reading and writing.
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