This article aims to study the application of artificial intelligence robots based on machine learning and visual algorithms in music classroom interactive experience assistance. In artificial intelligence robots, mob...
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This article aims to study the application of artificial intelligence robots based on machine learning and visual algorithms in music classroom interactive experience assistance. In artificial intelligence robots, mobile adaptive networks can be used to optimize the perception and decision-making abilities of robots. By continuously learning and adapting to environmental changes, robots can better understand and respond to the interactive needs of music classrooms, providing more accurate and targeted auxiliary services. By learning and analyzing rich training data, robots can possess higher-level cognitive and comprehension abilities. In terms of music recommendation, the K-nearest neighbor algorithm is used to recommend music works that are suitable for students. By analyzing students' music preferences and learning needs, robots provide personalized music recommendations to students based on this information, helping them better participate in and enjoy music classes. By applying machine learning and visual algorithms to music classroom interaction experiments, artificial intelligence robots based on machine learning and visual algorithms have the potential to assist in music classroom interaction experience, and teaching optimization strategies for music classrooms have been proposed.
Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion;This algorithm int...
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Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion;This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algorithms, and proposes an improved algorithm in view of multi-sensor fusion to improve tracking accuracy. The experiment shows that the average Root-mean-square deviation of the position of the improved mobile robot vision algorithm in view of multi feature fusion is 0.02 in nine data packets of the Tum dataset;The average Root-mean-square deviation of the position of the data packet successfully tracked by the traditional wheeled robot vision algorithm is 0.016;It improved the average accuracy by 11.11%, which is 31.03% higher than the average accuracy of the Manhattan wheeled robot vision algorithm. Compared to the multi feature fusion based vision improvement algorithm for mobile robots and the closed-loop detection based multi-sensor improvement algorithm, the accuracy of the closed-loop detection based multi-sensor improvement algorithm has increased by 0.655% and 10.47%, respectively. The outcomes indicate that the improved algorithm can improve the accuracy of mobile robot tracking, thereby expanding its application range.
This paper proposes a hierarchical reasoning method for shoreline extraction that relies solely on a camera and visual algorithms. First, the DeepLabV3+ algorithm is used to extract water bodies from images, and the C...
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Through a combination of wearable cameras, hardware accelerators, and algorithms, a vision-based automatic shopping assistant allows users with limited or no sight to select products from grocery shelves.
Through a combination of wearable cameras, hardware accelerators, and algorithms, a vision-based automatic shopping assistant allows users with limited or no sight to select products from grocery shelves.
In this demonstration we will present an application of a fast and robust face tracking algorithm implemented with a CNN Universal Machine chip. The application is the driving of a wheelchair according to predefined m...
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ISBN:
(纸本)9781424406395
In this demonstration we will present an application of a fast and robust face tracking algorithm implemented with a CNN Universal Machine chip. The application is the driving of a wheelchair according to predefined movements of the user's head. A 3D model of a wheelchair is used in order to demonstrate the driving mechanism.
An algorithm for fast and robust face tracking with the CNN Universal Machine is proposed in this paper. It is applied to a driving mechanism for a wheelchair with an on-chip implementation. A novel object tracking CN...
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ISBN:
(纸本)9781424406395
An algorithm for fast and robust face tracking with the CNN Universal Machine is proposed in this paper. It is applied to a driving mechanism for a wheelchair with an on-chip implementation. A novel object tracking CNN visual algorithm is introduced and employed in the tracking of multiple face features. The speed and robustness of this method are achieved due to the parallelism in the visual algorithm, and the tracking of multiple face features. The tracking algorithm is designed to achieve a high frame rate and exploit the specific properties of face features. The face tracking method proposed here was implemented on a Bi-I stand-alone cellular vision system and applied to a wheelchair driving mechanism. The template operations were trained and/or fine-tuned in order to generate chip-specific robust templates. In order to improve performance in environments with varying illumination, an adaptive image capture procedure was also introduced. Our simulations with a 3D model wheelchair showed that the final algorithm is capable of performing tracking with a frame rate of 92 frames/sec, which is supposedly enough for real-time driving in most of the real life situations.
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