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At present, most experimental teaching systems lack guidance of an operator, and thus users often do not know what to do during an experiment. The user load is therefore increased, and the learning efficiency of the students is decreased. To solve the problem of insufficient system interactivity and guidance, an experimental navigation system based on multi-mode fusion is proposed in this paper. The system first obtains user information by sensing the hardware devices, intelligently perceives the user intention and progress of the experiment according to the information acquired, and finally carries out a multi-modal intelligent navigation process for users. As an innovative aspect of this study, an intelligent multi-mode navigation system is used to guide users in conducting experiments, thereby reducing the user load and enabling the users to effectively complete their experiments. The results prove that this system can guide users in completing their experiments, and can effectively reduce the user load during the interaction process and improve the efficiency.

In middle school biology teaching and learning, there are many biological structures that students need to understand and master. Traditional classrooms mostly use video and PPT to teach, which is not conducive to students' understanding of three-dimensional structure, thus making it difficult to learn biological knowledge. In order to solve the problem of 3D biological structure learning, this paper proposes a natural interaction-based VR biology learning system method, which intelligently perceives the user's interaction intention and interacts with the user through virtual reality technology and multi-modal fusion technology. The innovation of this paper lies in: Firstly, a new VR biological learning system based on natural interaction is proposed. Through the fusion of gestures and speech, the user interacts naturally with the system. This method frees the user's hands from the constraints of the mouse and keyboard and the handle, enhancing the immersion of virtual reality. Secondly, this paper proposes a multi-modal fusion algorithm, which makes full use of the advantages of gestures and speech and compensates for their shortcomings, so as to better perceive the user's operational intention. It makes up for the shortcomings of the virtual learning system's lack of intelligence, so that users get a more convenient and more natural interactive experience. According to the user's experience feedback, the system proposed in this paper can better stimulate students' interest in learning, and can present the abstract three-dimensional knowledge in front of the user more intuitively. It has the good intelligence and the operation experience feeling, can reduce the user's use load effectively, the user operates more conveniently.

With the aging of the population, more and more old people need to live alone at home. In order to improve the robot's ability to understand the user's intention in the home environment, this paper proposes a knowledge-based multimodal intention active perception algorithm. The robot actively perceives the user's intention to interact before the user issues interactive commands. Finally, the algorithm was verified on Pepper robot. On the basis of knowledge, this algorithm can actively understand the user's intention by integrating multimodal information. First of all, visual intention inference and auditory intention inference are obtained by combining database and expert knowledge base. Then, the fusion results are obtained by using the multimodal intention active perception algorithm. The robot initiatively initiates interactions to request user feedback and perform corresponding actions. Experimental results show that the proposed multimode algorithm is superior to the single-mode algorithm. It improves the efficiency of human-computer interaction and reduces the burden of users, and is well received by test users.

With the rapid development of data-driven animation technologies, huge motion capture data has been accumulated. Motion capture data is a kind of spatio-temporal high dimensional data, which needs a lot of storage space. Efficient compression and transmission of motion capture data has become a hot topic in computer animation. In this paper, we induce the characteristics of motion capture data and the general processing pipeline of motion capture data compression algorithm. Then we review the research achievements in the field of motion capture data compression for the last twenty years. According to reduced dimensions, running platform, lossy or lossless, motion data format, environment contact processing and progressivity, we put these motion capture data compression algorithms into different categories. Finally, we look forward to the future research trend.

In the traditional teaching mode, the beginners generally feel that the concepts in the programming courses are abstract and the programming ability is difficult to improve. Analyzing the teaching contents and the key difficulties in the teaching activities, we put forward the mixed teaching mode which combines the MOOC teaching mode and the traditional classroom teaching mode. This teaching mode can make full use of the existing online resources to build courses, and focus on arousing the learners’ autonomous learning motivation and the mutual cooperation ability within study groups. The teaching practice shows that this mixed teaching mode can better improve the learning interest and practical programming ability of the students compared with the traditional teaching mode.

In order to solve the problem of teachers’ excessive energy dissipation due to interaction with teaching equipment in traditional classrooms, an interactive and intelligent teaching interface is proposed to enable teachers to use the gestures to give students a geometry lesson. The traditional algorithm of gesture recognition mainly consists of feature extraction and classifier, which requires human-designed features. The recognition is mainly based on static gesture or dynamic gesture singular state recognition algorithm. The recognition accuracy is not robust enough and different people Identification results do not have the universality and ease of operation. In order to solve this problem, we propose a multi-state gesture recognition algorithm based on the deep learning network, which combines the large database of hand gestures and the deep learning algorithms. The innovation of this algorithm is as follows: Aiming at the static gesture images, a sequence reduction algorithm is proposed. According to the sequence of dynamic gestures, the first and last frame fixed and intermediate frame traversal combination algorithm are proposed to get the dynamic and static fusion gesture training datasets, and then the dynamic and static fusion datasets are input to the deep learning network GoogLeNet for training. After repeated training, we found the optimal rule of deep learning network training. According to the optimization law, we got GoogLeNet_model which can recognize 23 kinds of dynamic and static fusion gestures, the recognition rate is 97.09%. We use this model in interactive teaching interface, and achieved good application effect.

A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset.

In this paper we propose a gaze zone estimation method using deep learning. Compared with traditional method, our method does not need the procedure of calibration. In the proposed method, a Kinect is used to capture the video of a computer user, which is pre-processed to suppress illumination variations. After that, haar cascade classifier is adopted to detect the face region and eye region. Then, the eye region is used to estimate the gaze zone on the monitor via a trained CNN (Convolution Neural Network). Experimental results show that the proposed method has a high accuracy, which can be applied in human-computer interaction.