Moving Target Tracking Algorithm Research Articles

Concrete structure assembly technology based on 3D intelligent image analysis

AbstractAt present, there is no standard practice for temporary connection of component installation. How to make temporary connections to components more efficient and accurate is the key to the construction of cast‐in‐place connection parts in concrete prefabricated buildings. In order to improve the assembly technology of concrete structures, this paper combines three‐dimensional intelligent image analysis technology for simulation, analyzes and compares several common electronic image stabilization algorithms, discusses the video image stabilization technology based on gray projection method in detail, and gives experimental results. Moreover, this paper analyzes and compares common moving target tracking algorithms, such as feature‐based tracking, region matching‐based tracking, dynamic contour‐based tracking, and 3D model‐based tracking. In addition, this paper studies the target tracking algorithm based on the Camshift algorithm, and constructs the assembly model of the intelligent concrete structure with the support of the algorithm. Through experimental verification, it is known that the performance distribution of the concrete structure assembly model based on 3D intelligent image analysis in experimental evaluation is between [81, 89], the experimental study shows that the concrete structure assembly model based on 3D intelligent image analysis can effectively improve the assembly effect of concrete structure.

Domain-knowledge-aided airborne ground moving targets tracking

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving target tracking algorithm. To improve the accuracy of multiple target tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as state constraints. The domain knowledge considered in this paper includes both road information extracted from a given map and local traffic regulations. Conventional track update method is modified to enhance the capability of recognition of temporarily track loss. A variable structure multiple model (VS-MM) method is developed to assign the road segment to a given target. The main contribution of this paper is that we establish a comprehensive framework for target tracking with the aid of domain knowledge and analyze the effect of diverse constraints on target tracking performance, while the limitations involve only 2-D target behaviors are considered, and the sophisticated interactions between targets are neglected. The effectiveness of proposed algorithm is demonstrated through extensive numerical simulations.

Moving Target Tracking Method Based on Improved Camshift

<p>Aiming at the problem that target occlusion and other disturbances in complex background will reduce the tracking accuracy of moving target, and even lead to tracking failure, this paper proposes a moving target tracking algorithm based on the improved Camshift algorithm. Firstly, Gaussian background is used to model the foreground image to improve the backprojection image, and then the interference of backprojection is removed to improve the tracking effect in complex background conditions. Secondly, Kalman filtering is utilized to predict the trajectory, which further improves the tracking accuracy of Camshift algorithm in occlusion condition. A lot of experiments are processed, and the results show that the proposed algorithm could effectively improve the tracking accuracy and meet the real-time requirements.</p> <p> </p>

Microscopic mechanical properties and fabric anisotropic evolution law of open graded gravel permeable base under dynamic loading

Under traffic dynamic load, the microscopic mechanical properties and the fabric anisotropic evolution of open graded gravel permeable base directly determine the ability of open graded gravel permeable base to resist external loads and maintain stability in service. Firstly, this article constructed a discrete element numerical model for open graded gravel permeable base, and verified the accuracy of the model through experiments. Then, the effects of gradation, nominal maximum particle size, friction coefficient between aggregate particles, and effective elastic modulus of aggregate on the microscopic mechanical properties, migration behavior, and the fabric anisotropic evolution of open graded gravel aggregate particles were studied in sequence. The research results indicate that the proposed image moving target tracking algorithm based on the three branches twin network can achieve precise tracking and measurement of particle microscopic migration behavior. The method proposed in this study for constructing a three-dimensional surface model of gravel particles based on the principle of multi view averaging and the scale invariant feature transformation algorithm can achieve rapid numerical reconstruction of the three-dimensional morphology of gravel particles at a low cost. The internal aggregate particles of intermediate grade gravel are more likely to form a stable network force chain topology structure that can effectively resist external loads. As the maximum nominal particle size increases, the topological structure of the contact force of gravel particles becomes sparse, and the proportion of strong contact force increases. Increasing the friction coefficient of the contact interface between aggregate particles can to some extent hinder the migration behavior of aggregate particles inside the graded gravel. As the effective elastic modulus of the aggregate increases, the internal network strong contact force chain topology of open graded gravel gradually forms. Under external loads, the migration speed and displacement of the aggregate particles inside open graded gravel decrease, and the degree of internal normal contact force and branch vector anisotropy also decrease.

Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

Synthetic aperture radar ground moving target indication (SAR-GMTI) is an attractive mode for radar system to obtain high-resolution regional images and detection of moving targets simultaneously. Moreover, the 360° observation capability of circular SAR (CSAR) makes it possible for moving target trajectory reconstruction. In order to address the problem that slow-moving targets, which are buried in strong stationary ground clutter, are difficult to be detected, a framework of multichannel SAR-GMTI is presented in this article. For ground moving target detection in the presence of stationary clutter, the proposed method first adopts clutter suppression interferometry and relaxation-based cyclic algorithm to suppress clutterand retrieve parameters (e.g., radial velocity and Doppler information). Then, to further reduce the false alarm rate, multiple moving target tracking algorithm is performed in range–Doppler domain. Finally, the moving target trajectory is reconstructed by using the proposed two-stage parameter estimation method based on Doppler characteristic and CSAR geometry, and the mathematical analysis of this method is performed. The proposed method is robust and insensitive to the signal-to-noise ratio. It does not rely on priori road information, so it has wide applicability, especially in military application. In addition, the framework is performed in echo domain and independent of imaging processing. The experimental results on simulated data are presented to evaluate the estimation accuracy of the proposed method, and the real data processing results are provided to demonstrate the validity and feasibility of the proposed method.

(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

With the vigorous development of contemporary information technology, the field of computer technology is constantly innovating and progressing. Among them, the field of computer vision has achieved more prominent development. Computer vision is a discipline that uses cameras and computers to imitate the structure of human organs, processes and analyzes the collected video sequences, and then makes corresponding behavior judgments. Moving object detection and tracking is an important branch of computer vision. Moving target detection and tracking has broad application space and development scenarios. Here, we aim to study the detection and tracking method of moving targets in a martial arts arena video based on non-scene target features. Based on this research theme, we combine the particle filter moving target tracking algorithm based on non-scene target features to carry out the moving target detection and tracking experiments in the video of the martial arts arena. The experiment concluded: compared with the conventional methods, the particle filter moving target tracking algorithm based on the non-scene target features can perform the following tasks without affecting the detection accuracy. It reduces the time for detecting and tracking the same moving target in the video of the martial arts arena by 1.5 min. It shows that the algorithm can improve the efficiency of non-scene moving target detection and tracking.

Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.

Volleyball Video Moving Target Tracking and Detection Algorithm Based on Multisensor Information Fusion

With the improvement of the level of sports competition technology, pure sports game broadcasting can no longer meet the requirements of various users. Users urgently need new technologies to meet the needs of quickly obtaining game information. The 5G Internet of Things is rapidly rising; volleyball is also gradually into people’s life. To solve the above problems, the research on the volleyball video moving target tracking and detection algorithm based on 5G Internet of Things communication and artificial intelligence becomes very important. This article is aimed at comparing the tracking and detection algorithms of volleyball video moving targets through 5G Internet of Things communication and artificial intelligence. In the process of detecting moving targets, several commonly used background detection methods are comprehensively compared and analyzed, and a method based on adaptive background difference is proposed. The algorithm uses Gaussian mixture model for background modeling, uses the OTSU threshold algorithm to determine the segmentation threshold T , and proposes a background update method that combines the expected full statistics and the L -close window to accelerate the background update speed, so that a more accurate sports prospect target. Experimental results show that the algorithm can detect and track moving targets in volleyball videos well in a fixed scene or when moving targets are not blocked, with a detection accuracy of more than 92%.

Research on tracking method of moving object under the dynamic background

In order to solve the problem that the traditional moving target tracking algorithm is vulnerable to interference and difficult to accurately track the target for a long time, we first introduce the common target tracking algorithm under the dynamic background, and then we determine the KCF algorithm which has better tracking effect and real-time performance. Then, aiming at the problem of target box offset in the longtime tracking, a tracking algorithm combining Kalman filter algorithm and KCF algorithm is proposed. Finally, the actual effect proves to be effective.

Erratum to “Through-Wall Moving Target Tracking Algorithm in Multipath Using UWB Radar”

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Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of Things (IoT) and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video.

High Speed Moving Target Tracking Algorithm based on Mean Shift for Video Human Motion

As an important research field of computer vision, there are a lot of achievements of human action recognition in recent Most of the researches are confined to the daily gentle action of human, such as walking jogging, sitting down. In sports video there were many specific characteristics about human actions, such as the upside pose of human, high speed, moving viewpoint. For human motion characteristics of sports video, a highspeed moving target tracking algorithm was proposed. Mean shift was an effective tracking algorithm, which was employed to track moving object in video, but it loses object frequently when the tracked object in the video moves rapidly. We advanced an approach to tracking object moving rapidly, and it can work effectively when the object was in cluttered environment. Experimental shows that the proposed algorithm can improve the tracking effect of mean shift algorithm and effectively track high-speed moving target.

Through-Wall Moving Target Tracking Algorithm in Multipath Using UWB Radar

Through-wall moving target tracking is an essential part of applications with the purpose of sensing moving human in the sealed building. However, the major challenge stems from the multipath effect in the actual environment, which creates errors in the range delay of moving target. In this letter, an effective through-wall moving target tracking algorithm in multipath is proposed. This method based on the correlation of human motion can restore and estimate the range delay of the moving target in the strong multipath interference. Both simulation and experiment results indicate that the proposed method can not only suppress multipath but also smooth the trajectory.

Rotation-Based Scale Adaptive Moving Target Tracking Algorithm

Rotation-Based Scale Adaptive Moving Target Tracking Algorithm

Research on Real-time Tracking Algorithm of Moving Objects Based on Machine Vision

With the rapid development of -IT and digital image processing technology, moving target tracking based on machine vision has always been a very active research topic in the fields of computer vision, robot and artificial intelligence, image processing and pattern recognition. As a research field with a wide application background, moving target tracking based on machine vision has attracted a large number of researchers. Many foreign research institutions also list it as an important research direction, and have achieved a lot of results. Artificial intelligence and other technologies have been widely used in various fields. At present, the moving target tracking algorithm based on filtering theory has attracted much attention. In this paper, a template matching method based on multi association is proposed for tracking. Experiments show that the separation accuracy of the algorithm is enhanced, the tracking effect is good, and can meet the real-time performance.

Detection and Tracking of Human Motion Targets in Video Images Based on Camshift Algorithms

For the traditional moving target tracking algorithm, it is difficult to detect the target area accurately because of the influence of environmental factors. Therefore, manual selection of moving targets is often used, but this method is not intelligent enough, and has strong subjectivity and experience, so the data obtained is not convincing enough. For this reason, based on the multi-frame subtraction method, the connected region search is used to select the appropriate moving target and calibrate the target area, and then the moving target is determined and tracked. At the target tracking stage, the fusion feature vectors in the calibrated area are updated in real time to improve the accuracy and robustness of the follow-up tracking algorithm. Experiments show that the method is feasible. In the stage of target detection, the improved algorithm filters and calibrates the target area on the premise of increasing a small amount of computation. In the stage of target tracking, the computational area is reduced to the calibration and tracking range, which improves the efficiency of the algorithm and guarantees the stability and robustness of the algorithm.

The application of CAMSHIFT algorithm based on kernel function in laser tracking moving target

According to the characteristics of laser technology, a fast moving target tracking algorithm is designed. Using the spatial information of laser diffuse reflection light points, by adding the kernel function of CAMSHIFT algorithm, the weight of light points in the center of the target is increased by using the weighted histogram, so as to improve the convergence speed of the algorithm and the tracking accuracy of the target. The simulation results show that the improved algorithm can adapt to more complex tracking environment quickly and has better anti-interference, agility and prediction ability.

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

Multiple Sensor Linear Multi-Target Integrated Probabilistic Data Association for Ultra-Wide Band Radar

Ultra-Wide Band (UWB) radar has a number of advantages of resolving multipath, exceptional spatial resolution, and ranging performance. However, several difficulties are confronted for multiple target tracking using UWB radars such as clutter signals which contaminate target signals, and unidentified number and behavior of the targets. Hence, this paper presents to develop a multiple moving target tracking algorithm, consisting of preprocessing and multiple target tracking steps. In the preprocessing step, static clutter reduction and constant false alarm rate (CFAR) detection extract the target candidate range measurements from each UWB radar. Then, two multiple target tracking (MTT) steps are developed: range- based MTT and position-based MTT. The range-based MTT is mainly based on existing linear multi-target integrated probabilistic data association (LM-IPDA) from each UWB radar measurement. Then the outputs of each LM-IPDA are gathered in the positioning center to estimate the position of multiple targets. On the other hands, the position-based MTT is based on multiple sensor LM-IPDA (msLM-IPDA) as an accurate target tracking method for various uncertainties by improving the probabilistic model of LM-IPDA. The tracking performance of two MTT methods is investigated with both numerical simulation and experiments.

WITHDRAWN: Moving target tracking algorithm and trajectory generation based on Kalman filter in sports video

Abstract This paper takes the video of volleyball game collected by fixed camera as an example to study the moving targets in the volleyball video sequence, complete the prediction and tracking of volleyball and players, and trace the depiction. For moving objects tracking, firstly, the ID and location information on the objects blob is received through image segmentation. Secondly, tracking algorithm is a combination of centroid tracking algorithm and Kalman filter. The algorithm can provide players and volleyball object tracking. In the process of tracking, Kalman filter is imposed to forecast the motion information to improve the tracking precision. On the basis of the moving object tracking, this paper takes path generating the module to describe players tracks and preserve motion information. Based on the principle of double camera measure to reconstruct 3D information by two images taken from different angles at the same time, 3D information is used to match the volleyball trajectory. Experimental results show that the rate of accuracy of moving target tracking algorithm higher than 92%, the algorithm can realize the real-time and stable while ensuring the accuracy, providing the necessary basis for accurately video analysis.