Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation

Abstract

The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people’s actual needs to a certain extent. With the assistance of Lightweight Deep Learning (LDL) models, Interactive Artistic Installation (IAI) shows excellent creative potential in terms of dimension, space, and sense. Virtual Vision Sensing Technology (VST) explores the emotional semantics in the human-machine environment with the help of interactive art, finds the emotional interaction elements between human and machine, and promotes Human-Computer Interaction (HCI). From the perspective of the media elements of interactive art, this paper reviews the virtual VST that subverts the expression of interactive art. Then, from the perspective of artistic creation, the impact of virtual VST on IAI thinking, methods, and artistic experience is analyzed. Thereupon, a scene construction method is designed where the physical equipment is premodeled. The model is loaded in real time with visual information. The proposed method does not require complex vision and laser scanning equipment or high-configured computer systems. The proposed new media IAI model realizes the real-time loading of the scene model. According to the physical equipment dynamic information obtained by the visual data acquisition system, the proposed method can keep the virtual scene and physical models in motion synchronization. Finally, experiment results corroborate that the environment will significantly interfere with the experimental results. The training data set with boundary occlusion will be more suitable for model training and better test results (about 97% accuracy). Hence, the research content can make the Virtual Reality works have better performance, especially the sense of experience from the perspective of aesthetics. Meanwhile, it also enriches the research theory in the field of new media art installation technology.

Interactive art design with deep learning and information fusion technology

“…As is well known, modern interactive art design is a creative form that combines art and technology. In response to this, Luo X pointed out that in the context of rapid development of information technology, new media has become an important medium for modern interactive art design [17]. From Table 3, it can be seen that due to technological and conceptual changes, the differences between the two art designs can be divided into four main aspects: audience participation, artistic experience, media technology, and creative process.…”

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

“…Recent deep-learning trends show fundamental understanding among researchers about the need for lightweight deep-learning algorithms for IoT [273]. Several advances reflect this trend, such as Alibaba introducing the open-source mobile neural network [274], a lightweight deep-learning model for HAR using smart "things" on edge [275], Mobi-Face, ShuffleFaceNet for face recognition on mobile devices [274,276], lightweight machine learning for IoT Systems (LIMITS) [277], CardioXNet, a lightweight deep-learning framework for heart-disease prediction [278], lightweight deep learning-based virtual vision sensing technology [279], and lightweight convolutional neural networks (CNNs) [280,281]. In the future, we expect more development in this direction, as all or most IoT devices cannot match conventional deep-learning systems in terms of processing, memory, or power requirements.…”

Designing and Developing Android-based E-Book as a Guide for Students in Making Smart-Key: A Learning Media for Electronic Circuits Course

“…Therefore, as one of the facilitators in learning, teachers and lecturers should have the ability to utilize technology in learning, such as using it as a learning media. (Fan & Ye, 2022;Leow & Neo, 2014;Lu, Wu, & Huang, 2022;Luo, 2022;Vinogradova et al, 2022).…”