With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the m...
详细信息
ISBN:
(数字)9781665490092
ISBN:
(纸本)9781665490092
With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as imageprocessing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or imageprocessing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an
The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF h...
详细信息
ISBN:
(纸本)9798350337327
The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.
Aiming at the problem of large noise and artifacts generated by the first 3D image virtual reconstruction system, a 3D image virtual reconstruction system based on visual communication technology is proposed. In this ...
详细信息
Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plant...
详细信息
Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, wh...
详细信息
This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategi...
详细信息
ISBN:
(数字)9798350377033
ISBN:
(纸本)9798350377040
This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategies to improve the performance and accuracy of image data processingsystems. First, by designing a deep CNN architecture, important features in the image are extracted to achieve efficient completion of image recognition and classification tasks. Subsequently, a new multi-level data processing method is proposed, which can optimize image data at different levels, thereby improving processing speed and reducing noise interference. Through a series of simulation experiments, the results show that the image classification accuracy of the algorithm is improved by about 12 % , from 85.6% of the traditional method to 97.3%. In addition, the processing efficiency is improved by about 20%, the data processing time is reduced from 2.5 seconds of the traditional method to 2 seconds, and the stability of the system is significantly enhanced by introducing optimization strategies, and the stability is improved by about 18%. The optimized algorithm shows significant advantages in both accuracy and efficiency, meeting the needs of efficient intelligent imageprocessingsystems.
In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compa...
详细信息
ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
In recent years, the application of artificial intelligence (AI) techniques for fire detection has gained significant attention due to its potential for enhancing early fire detection systems. This study aims to compare the performance of deep learning convolutional neural networks (CNN) and support vector machine (SVM) machine learning algorithms in the context of fire detection. We present a comprehensive analysis and evaluation of the two approaches, highlighting their strengths and weaknesses, and discussing their potential for real-world fire detection applications.
Nature-inspired algorithms (NIAs) are very well defined for intuitive imageprocessing operations. Among various nature-inspired algorithms, elephant herding optimization (EHO) is most preferably used as its applicati...
详细信息
As uncrewed aerial systems continue to grow in popularity and importance, the long-term and scalable use of these systems for remote sensing and imagery data collection remains a valuable and achievable goal. To enabl...
详细信息
ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
As uncrewed aerial systems continue to grow in popularity and importance, the long-term and scalable use of these systems for remote sensing and imagery data collection remains a valuable and achievable goal. To enable these systems at scale, real-time onboard imagery processing is required. To determine the feasibility of real-time remote sensing systems, many factors must be accounted for, including the ability of the sensing and processingalgorithms to operate on and collect data from real-world scenes and deliver actionable intelligence to the data consumer. In this paper, a holistic simulation system based on ROS 2 and Gazebo is presented, which allows for real-time processingalgorithms to be tested and proven for flight in an accurate and extensible way. By using ROS 2 and USU AggieAir's STARDOS platform, it is possible to show how the remote sensing system and onboard, real-time processingalgorithms are applicable to the aerial remote sensing task (i.e. it can demonstrate feasibility for physical deployment based on accurate simulated data processing).
Histogram equalization is a method of contrast adjustment in imageprocessing using the image’s histogram. However, as modern imaging systems become more complex, these traditional algorithms for histogram equalizati...
详细信息
ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Histogram equalization is a method of contrast adjustment in imageprocessing using the image’s histogram. However, as modern imaging systems become more complex, these traditional algorithms for histogram equalization are no longer efficient. In response to this problem, researchers have studied several strategies for improving the performance of histogram equalization in digital images. An option is to use parallel processing and multi-threading approaches to distribute the computational burden, thereby speeding up the execution of histogram equalization. Another methodology includes using machine learning algorithms to adapt histogram equalization parameters according to the input image. Furthermore, using advanced hardware architectures like Field Programmable Gate Arrays (FPGA), Graphic processing Units (GPU), or Application Specific Integrated Circuits can significantly enhance the speed and efficiency of a Histogram Equalization. The performance optimization techniques have provided encouraging results, which significantly refine imageprocessing time and visual perception. Modern imaging systems may benefit tremendously from their use in the new age.
暂无评论