The real-time obstacle detection and path adjustment system for autonomous robots presented in this paper was created using OpenCV. The combination of imageprocessing techniques enables the robot to identify and navi...
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ISBN:
(数字)9798331527396
ISBN:
(纸本)9798331527402
The real-time obstacle detection and path adjustment system for autonomous robots presented in this paper was created using OpenCV. The combination of imageprocessing techniques enables the robot to identify and navigate around any obstacle efficiently. The main functions include real-time image acquisition; where the robot constantly takes pictures of its surroundings, and obstacle detection; which uses edge detection and contour analysis for proper identification of hazards in the environment. The method entails that once an object is detected, the robot's route is reshaped with sophisticated algorithms that are aimed at optimizing navigation as well as minimizing collision probabilities. A wide range of challenging settings such as static or dynamic environments were subjected to rigorous testing on the proposed system. The results indicate that it is robust and effective in detecting obstacles reliably to enhance navigation based on its capacity to make adjustments during operation time (discovered employing experimentation that took place in these settings).
In the realm of computer-assisted learning, video tutorials have gained significant traction for teaching various sports, including basketball. However, these tutorials often suffer from issues such as lack of standar...
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ISBN:
(数字)9798350374407
ISBN:
(纸本)9798350374414
In the realm of computer-assisted learning, video tutorials have gained significant traction for teaching various sports, including basketball. However, these tutorials often suffer from issues such as lack of standardization, clarity, and effectiveness. To address these challenges, this study presents a novel approach: a basketball action recognition system built upon an enhanced OpenPose algorithm. This system efficiently processes videos or images captured by learners, identifies crucial action frames, reconstructs human skeletal structures, computes joint angles, compares them with standard actions, and offers real-time posture correction guidance. Experimental validation showcases the system’s potential as a valuable tool for learning and honing basketball techniques. Furthermore, its adaptability suggests broader applications across diverse sports education contexts, emphasizing its practical significance and potential impact. The integration of machine learning algorithms ensures continuous improvement and customization, catering to individual learning needs and enhancing overall learning experiences.
Enhancing videos or images on resolution, frame rate, dynamic range, color gamut, noise, or scratches removing have attracted the interest of experts. Efficient image quality assessment methods for these image enhance...
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Iris recognition has fascinated the attention of several real-life applications to offer secured and reliable human authentication. However, these systems are exposed to a diverse range of security breaches that inclu...
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The liquid level detection in a transparent bottle is the requirement in the food, Beverage industry, Scene understanding, Chemical Laboratories, and Forensic sciences. The liquid level detection indicates the qu...
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Wavelet theory has been widely applied in imageprocessing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel ima...
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ISBN:
(数字)9798350374315
ISBN:
(纸本)9798350374322
Wavelet theory has been widely applied in imageprocessing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. This method significantly enhanced image clarity without increasing computational demand. This article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024.
Solar energy production prediction is an essential prerequisite for the successful implementation of renewable energy sources and the elaboration of sustainable strategies. The paper provides a detailed overview of ar...
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As a key component of point cloud acquisition technology, LiDAR (Light Detection and Ranging) acts as an eye in autonomous driving. The vehicle-mounted LiDAR point cloud system is the prerequisite foundation for the v...
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Target tracking at high-speed conditions is highly desirable for embedded systems, such as visual navigation. However, existing methods face challenges in achieving real-time processing due to algorithm complexity and...
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ISBN:
(数字)9798350355413
ISBN:
(纸本)9798350355420
Target tracking at high-speed conditions is highly desirable for embedded systems, such as visual navigation. However, existing methods face challenges in achieving real-time processing due to algorithm complexity and computational limitations. To address this issue, we propose an anti-occlusion scale-adaptive correlation tracker (OSCT) based on a Kernelized Correlation Filter (KCF), which considers tracking confidence and multiple features in embedded systems. Specifically, we employ a discriminative correlation filter to estimate the scale of the tracking target. Subsequently, our tracker dynamically adjusts the learning rate by evaluating the confidence of tracking results. Moreover, the OSCT uses a re-detector to re-detect the target in case of tracking failure. Instead of relying on a single feature, our method integrates multiple features to address variation and occlusion in tracking objects. Experimental results demonstrate that: 1) OSCT achieves an average precision of 0.778 at 20 pixels, representing an improvement of over 8% compared to other high-speed trackers. 2) The average running speed of OSCT is 32 FPS, outperforming LCT (26 FPS). Therefore, our approach is well-suited for long-term tracking on embedded platforms.
Recognizing human activities and behavior is a cutting-edge field of research because of the complexity and limited availability of data. Our research involves the use of the trending YOLOv8s deep learning algorithm f...
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