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Users of the digital image correlation method are faced with the problem of poor operability, low repeatability, and lack of standardized specifications for spraying speckles. To solve the problem, the research proposed a rock deformation measurement method that obviates the need to spray speckles. A local binary model was established by using the local binary pattern (LBP) operator based on deep texture features on rock surfaces. The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability. Based on the LBP digital speckle pattern, the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks. The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone. Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks. The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles: this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

Combined with the modern development form of traditional cultural and creative design, this paper proposes to use digital art elements to assist traditional cultural and creative design. The symbols of cultural and creative design are extracted using digital-assisted design technology, and the grayscale image extraction method is chosen to process the images of cultural and creative design. Then, combined with the YOLO v3 algorithm, CMT algorithm, and spatial mapping to realize three-dimensional tracking of real objects and real-time tracking interactions in mixed reality, adding digitally assisted experience of cultural symbols. With that, the theoretical model of digital art elements and traditional cultural and creative design is designed, and the procedural method of the entire cultural and creative design is analyzed. Online shopping consumer evaluations are selected, high-frequency words are classified, and consumers’ emotional tendencies are analyzed as well. The comprehensive scores of digital art and cultural and creative products are calculated using sense-quality evaluations. Consumers’ positive and negative emotions accounted for 75.72% and 17.49%, respectively, with the general negative emotion field accounting for the highest proportion of 10.54% and the medium and high negative emotion fields accounting for a lower proportion. It can be seen that the response of digital art elements to the cultural and creative design market is better, and its innovative design has a certain consumer market.

With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.

As the first step of vehicle detection and recognition system, how to quickly and accurately detect the vehicle in a picture is directly related to the subsequent vehicle application research. In order to improve the processing speed of vehicle detection, reduce the false alarm rate of detection, and get better results, the method is applied in real scene, this paper carried out in-depth research on this. Collect traffic and urban road surveillance videos as experimental data, of which 2000 were positive samples and 2000 were negative samples. Firstly, a vehicle image preprocessing is carried out on the collected experimental data, and the image feature is extracted based on gray image and improved AdaBoost algorithm, and then the image enhancement is realized by using multi-scale Retinex. Using this method, we can make the image processing accord with the nonlinear characteristics of the human eye to the brightness response, and avoid the distortion of the image directly processed by Fourier transform. In order to improve AdaBoost classifier, it is necessary to use local binary edge features and train the collected feature samples. In order to highlight the vehicle target and ignore the background, we need to use a selective graying way, which is based on the H component of HSV space. The experimental results show that the accuracy of AdaBoost classifier reaches 85.8%, the recall rate is 80.9%, and the comprehensive performance is very high, which can meet the performance requirements. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a fast-growing research field. In recent years, it has attracted considerable research attention. The generation of large-scale sensor networks interconnected hundreds of sensor nodes, opening up some technical challenges and huge application potential. This article mainly introduces multi-target tracking and detection based on wireless sensor network. This paper studies the positioning methods and target tracking algorithms of wireless sensor network nodes. It mainly studies the positioning algorithms based on ranging in the positioning algorithms in detail, analyzes the advantages and disadvantages of the algorithms, and analyzes the system models and related targets in the target tracking algorithms. The filtering has been studied in detail. In addition, a tracking algorithm under the mixed linear and non-linear motion of the moving target is also proposed, namely the hybrid filtering algorithm. This algorithm makes the motion state of the tracked moving target no longer restricted, and can freely switch between linear motion and nonlinear motion. The experimental results in this paper show that Kalman filter can effectively track moving targets without sudden changes in speed. When the mobile robot switches the grid, it will bring about the switching of the observation model. Compared with the least squares positioning algorithm, the smooth switching rate of the Kalman filter positioning algorithm is increased by 24%. When the three robots are running at a speed of 0. 5m/s in the monitoring area, the system can track the target in real time and send the positioning result to the robot to provide position navigation for the next formation feedback control.

One of the important factors in real-time tracking of the moving radar targets is the speed of the algorithm. In the multi-model particle filters (MMPFs) which is frequently preferred tracking of such targets, the numbers of particles and motion models are important parameters determining the speed of the filter. Reducing the number of particles and/or the model transitions processes as much as possible will facilitate real-time tracking of moving targets by accelerating the algorithm. In this study, for reducing the time cost of the MMPF, a new approach called weighted statistical model selection (WSMS) which reduces the number of model estimation calculations is proposed. A new basic MMPF algorithm that allows the use of the WSMS approach is also constituted. In order to evaluate the success of the WSMS; the MMPFs integrated with the WSMS, are simulated for different noise variances, particle numbers, and scenarios. The simulation results are compared based on processing time and prediction error criterions. The results demonstrate that the WSMS approach increases the speed of the algorithm by reducing the processing time at high rates without any change in the prediction error and, thus it can be used in real-time tracking of the moving targets.

This paper studies the real-time monitoring of rainfall and atmosphere characteristics and college students' physical training based on target tracking algorithm. In this paper, we use the target tracking algorithm, combined with the GNSS observation stations of BJFS, LHAZ, URUM, CUSV, and PIMO, to calculate the change of rainfall atmosphere characteristics of ULUM station, and then get the PWV above the ULUM station, and compare the PWV obtained by the radiosonde station with the ground-based GNSS inversion results to verify the change of rainfall atmosphere characteristics based on the target tracking algorithm. In order to solve the problem of inaccurate tracking and positioning when the background is cluttered, a target tracking algorithm based on channel reliable local rank transform is proposed. The local rank transform feature combined with lab three-channel image is used to form 16-dimensional feature channel, from which the effective feature channel is selected for fusion to enhance the expression ability of the algorithm for target features. The experimental results show that, compared with the feature channel selected by reliability coefficient, the local rank transform feature channel selected by experience has better tracking effect in specific scene and more accurate position estimation of target. At the same time, with the transformation of China from a sports power to a sports power, sports training is scientific, science and technology help sports, and the guarantee system of sports science integration is gradually improved. The state attaches great importance to the role of science and technology in the training of college athletes, and has purchased a set of Firstbeat sports monitoring system to monitor the athletes' heart rate in real time. A scientific research group is set up to provide scientific and technological services, record the heart rate of athletes during training and competition, so as to determine the "load characteristics" of athletes, and explore the monitoring of athletes' training with heart rate as the object, so as to improve the training quality of athletes. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economic construction, large-scale infrastructure construction has been carried out in the slope area. However, due to the poor geological environment, concentrated rainfall, and limited land resources in the region, a large number of slope toe excavation and graded slope cutting are carried out in the process of engineering construction. The unreasonable excavation method destroys the original slope shape and affects the stability of the slope, which brings many geological disasters in the loess area. Therefore, it is of great significance to analyze the influence of rainfall and excavation on the deformation and stability of loess slope. Considering the practical application, this paper also considers the target tracking on FPGA. The target tracking algorithm on FPGA must be reliable, real-time, meet certain tracking accuracy, and have certain operability. The proposed target tracking algorithm combines Harris method and optical flow method. Firstly, Harris method is used to extract some target angle features, and then optical flow method is used to match angle features more accurately for subsequent video frames. When the target is rotated or twisted, the barycenter algorithm is used to calculate the barycenter of the matching features. Based on the above research, this paper draws the following conclusions: the overall level of green economy efficiency is unbalanced; There is a U-shaped relationship between urban economic development and environmental pollution; The main influencing factors of urban green development efficiency are industrial structure, economic development degree, opening degree, financial development level, and human capital. [ABSTRACT FROM AUTHOR]

One of the important factors in real-time tracking of the moving radar targets is the speed of the algorithm. In the multi-model particle filters (MMPFs) which is frequently preferred tracking of such targets, the numbers of particles and motion models are important parameters determining the speed of the filter. Reducing the number of particles and/or the model transitions processes as much as possible will facilitate real-time tracking of moving targets by accelerating the algorithm. In this study, for reducing the time cost of the MMPF, a new approach called weighted statistical model selection (WSMS) which reduces the number of model estimation calculations is proposed. A new basic MMPF algorithm that allows the use of the WSMS approach is also constituted. In order to evaluate the success of the WSMS; the MMPFs integrated with the WSMS, are simulated for different noise variances, particle numbers, and scenarios. The simulation results are compared based on processing time and prediction error criterions. The results demonstrate that the WSMS approach increases the speed of the algorithm by reducing the processing time at high rates without any change in the prediction error and, thus it can be used in real-time tracking of the moving targets. [ABSTRACT FROM AUTHOR]

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real-time requirements of UAV target tracking task. [ABSTRACT FROM AUTHOR]

The on-board target tracking of unmanned aerial vehicle (UAV) relies on on-board computer to process the collected ground image information, and then use the target tracking algorithm to achieve the target tracking function, which provides the user with target position, trajectory and other information. UAVs are limited by load and power consumption. Due to the high resolution and frame rate of UAV's image collection system, traditional target tracking platforms cannot meet the requirements. As a result, the real-time nature of the tracking platform urgently needs to be resolved. This study designs and implements the airborne ground target tracking accelerator for UAV. The kernelised correlation filters (KCFs) algorithm with low computational complexity and precision is selected to achieve the airborne ground target tracking function and its performance bottleneck is analysed. In order to improve the real-time performance of the tracking task processing, the ZYNQ platform based on ARM and field programmable gate arrays is chosen to achieve the hardware acceleration of the KCF algorithm. The KCF algorithm is implemented in hardware using the high-level synthesis technology, and a high-speed data transmission link is established. Experiments show that the design can achieve a tracking rate of more than 30 FPS under 960 × 540 resolution, which meets the real-time requirements of UAV target tracking task.

Considering the power load in the system has the characteristics of random variation, and the prediction of the power load is the key to ensure the safe operation of the power. At present, there are many methods of power load analysis, based on the computer video capture equipment and target tracking technology, the power load analysis has a good performance in the application. Therefore, in this paper, the algorithm of target tracking under complex scenes of power system operation was researched, its specific application in power load analysis was discussed, and then, the multi-target tracking, irregular target tracking and other technical problems of the target tracking algorithm in the application process in the real world were solved. According to taking the power load analysis of a park grid system as the practical application object, it proved that the research of the target tracking algorithm had a good application effect in the real power load analysis. [ABSTRACT FROM AUTHOR]

The traditional target tracking algorithm is relatively simple, and the tracking effect is not ideal for the interference target. Therefore, in this paper, an target tracking algorithm based on extended Kalman filtering is proposed. First of all, bionic three-dimensional (3D) image feature in virtual reality is extracted; then, the target tracking algorithm based on extended Kalman filtering is used for weight selection and modification of 3D image, to zoom in and out of the window, so as to determine the position of the 3D image features. Experimental results prove that the algorithm has good robustness in the light changes and color interference, and it can meet the real-time requirements of tracking system. [ABSTRACT FROM AUTHOR]

The primary problem of tracking filtering algorithms is the tracking stability and effectiveness of target states. Based on the particle filter, an adaptive strong tracking particle filter algorithm is proposed in this study. According to the residual between actual measurement values and predicted measurement values of every moment, adjustment of the forgetting factor and the weakening factor is adaptively conducted. Then, by calculating the fading factor, transfer covariance matrix and filter gain of the system are obtained to estimate the particles state value. Updating the importance density function can alleviate the degradation phenomenon of particle filter, and it contributes to effective estimation for the optimal state value of a target. The simulation results demonstrate that the proposed algorithm provides a better tracking precision. In addition, when the target states make mutations, the proposed algorithm can track the mutation states of moving targets effectively and improve the stability of the system. [ABSTRACT FROM AUTHOR]