Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

With the continuous progress of science and technology, the sport of roller skating has developed rapidly and the technical level of the game has become higher and higher. Its sport performance has been rapidly improved. However, China’s roller skating is relatively late, and there is still a certain gap compared with many Western developed countries. In order to improve the performance of China’s roller skating, this study takes the representative Chinese and foreign excellent speed skaters as the research object and compares the sprinting technology of Chinese and foreign excellent speed skaters by using image measurement and image analysis to obtain the kinematic parameters and data of the athletes’ sprinting technology in the competition state. In view of the problem that the current video target tracking algorithm is easy to follow multiple targets, a video multiobject detection and tracking algorithm with improved tracking learning detection (TLD) is studied with the skater in the video as the research object. For the lost target, the prediction function of Kalman filter algorithm is used to track the trajectory of the typical target in the video, and the trajectory tracked by Kalman filter algorithm is used to compensate the lost part of TLD algorithm, so as to obtain the complete trajectory of the typical target in the video to improve the accuracy of video multiobject tracking. Since the existing trajectory prediction algorithms have the limitation of poor accuracy, a social-long short-term memory (Social-LSTM) network-based video typical target trajectory prediction algorithm is proposed to predict the trajectory sequences of typical targets to be detected by incorporating the contextual environment information and the interaction relationship between multiple target trajectories into the Social-LSTM network. The simulation results show that the proposed trajectory prediction algorithm outperforms the traditional LSTM algorithm, Hidden Markov Model Algorithm, and Hybrid Gaussian model algorithm, which is helpful to improve the accuracy of video roller skater target trajectory prediction, and the tracking success rate is 0.98.

Adaptive Kalman Filter with L2 Feedback Control for Active Suspension Using a Novel 9-DOF Semi-Vehicle Model

In order to further improve driving comfort, this paper takes the semi-vehicle active suspension as the research object. Furthermore, combined with a 5-DOF driver-seat model, a new 9-DOF driver seat-active suspension model is proposed. The adaptive Kalman filter combined with L2 feedback control algorithm is used to improve the controller. First, a discrete 9-DOF driver seat-active suspension model is established. Then, the L2 feedback algorithm is used to solve the optimal feedback matrix of the model, and the adaptive Kalman filter algorithm is used to replace the linear Kalman filter. Finally, the improved active suspension model and algorithm are verified through simulation and test. The results show that the new algorithm and model not only significantly improve the driver comfort, but also comprehensively optimize the other performance of the vehicle. Compared with the traditional LQG control algorithm, the RMS value of the acceleration experienced by the driver’s limb are, respectively, decreased by 10.9%, 15.9%, 6.4%, and 7.5%. The RMS value of pitch angle acceleration experienced by the driver decreased by 6.4%, and the RMS value of the dynamic tire deflection of front and rear tire decreased by 32.6% and 12.1%, respectively.

Design and Optimization of Parallel Algorithm for Kalman Filter on SW26010 Many-Core Processors

Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, financial industry, big data and so on. On Sunway TaihuLight platform, we present an optimized Kalman filter parallel algorithm which is according to new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for Kalman filter algorithm based on seven-level pipeline of SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five data sets, and is also improved compared to the algorithm on GPU.

A Cooperative Localization Method based on V2I Communication and Distance Information in Vehicular Networks

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

An Intelligent Networked Car-Hailing System Based on the Multi Sensor Fusion and UWB Positioning Technology under Complex Scenes Condition

In order to solve the problems of difficulty and long times to pick up cars in complex traffic scenes, this paper proposes an intelligent networked car-hailing system in complex scenes based on multi sensor fusion and Ultra-Wide-Band (UWB) technology. UWB positioning technology is adopted in the system, and the positioning data is optimized by the untraceable Kalman filter algorithm. Based on the environment perception technology of multi sensor fusion, such as machine vision and laser radar technology, an anti-collision warning algorithm was proposed in the process of car-hailing, which improved the safety factor of car-hailing. When the owner enters the parking lot, the intelligent vehicle can automatically locate the owner’s position and drive to the owner without human intervention, which provides a new idea for the development of intelligent networked vehicles and effectively improves the navigation accuracy and intelligence of intelligent vehicles.

Analysis and Forecast of the Number of Deaths, Recovered Cases, and Confirmed Cases From COVID-19 for the Top Four Affected Countries Using Kalman Filter

COVID-19 is a virus that spread globally, causing severe health complications and substantial economic impact in various parts of the world. The COVID-19 forecast on infections is significant and crucial information that will help in executing policies and effectively reducing the daily cases. Filtering techniques are important ways to model dynamic structures because they provide good valuations over the recursive Bayesian updates. Kalman filters, one of the filtering techniques, are useful in the studying of contagious infections. Kalman filter algorithm performs an important role in the development of actual and comprehensive approaches to inhibit, learn, react, and reduce spreadable disorder outbreaks in people. The purpose of this paper is to forecast COVID-19 infections using the Kalman filter method. The Kalman filter (KF) was applied for the four most affected countries, namely the United States, India, Brazil, and Russia. Based on the results obtained, the KF method is capable of keeping track of the real COVID-19 data in nearly all scenarios. Kalman filters in the archetype background implement and produce decent COVID-19 predictions. The results of the KF method support the decision-making process for short-term strategies in handling the COVID-19 outbreak.