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

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shaolou Duan ◽  
Lingfeng Meng ◽  
Delong Ma ◽  
Liangyu Mi
Keyword(s):  
Kalman Filter ◽  
Gaussian Model ◽  
Sport Performance ◽  
Tracking Algorithm ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Target Trajectory ◽  
Kalman Filter Algorithm ◽  
Lstm Network ◽  
Model Algorithm

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.

scholarly journals Improving Accuracy of the Kalman Filter Algorithm in Dynamic Conditions Using ANN-Based Learning Module

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 94 ◽  
Author(s):  
Israr Ullah ◽  
Muhammad Fayaz ◽  
DoHyeun Kim
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Historical Data ◽  
Mean Squared Error ◽  
External Factors ◽  
Prediction Algorithm ◽  
Dynamic Conditions ◽  
Learning Module ◽  
Prediction Algorithms ◽  
Kalman Filter Algorithm

Prediction algorithms enable computers to learn from historical data in order to make accurate decisions about an uncertain future to maximize expected benefit or avoid potential loss. Conventional prediction algorithms are usually based on a trained model, which is learned from historical data. However, the problem with such prediction algorithms is their inability to adapt to dynamic scenarios and changing conditions. This paper presents a novel learning to prediction model to improve the performance of prediction algorithms under dynamic conditions. In the proposed model, a learning module is attached to the prediction algorithm, which acts as a supervisor to monitor and improve the performance of the prediction algorithm continuously by analyzing its output and considering external factors that may have an influence on its performance. To evaluate the effectiveness of the proposed learning to prediction model, we have developed the artificial neural network (ANN)-based learning module to improve the prediction accuracy of the Kalman filter algorithm as a case study. For experimental analysis, we consider a scenario where the Kalman filter algorithm is used to predict actual temperature from noisy sensor readings. the Kalman filter algorithm uses fixed process error covariance R, which is not suitable for dynamic situations where the error in sensor readings varies due to some external factors. In this study, we assume variable error in temperature sensor readings due to the changing humidity level. We have developed a learning module based on ANN to estimate the amount of error in current readings and to update R in the Kalman filter accordingly. Through experiments, we observed that the Kalman filter with the learning module performed better (4.41%–11.19%) than the conventional Kalman filter algorithm in terms of the root mean squared error metric.

scholarly journals Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example

2021 ◽  
Vol 25 (3) ◽  
pp. 1005-1023
Author(s):  
Wanting Qin ◽  
Jun Tang ◽  
Cong Lu ◽  
Songyang Lao
Keyword(s):  
Kalman Filter ◽  
Moving Objects ◽  
Short Term Memory ◽  
Effective Means ◽  
Prediction Algorithm ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

AbstractTrajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.

A small UAV tracking algorithm based on AIMM-UKF

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhiwen Hou ◽  
Fanliang Bu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Velocity Model ◽  
Tracking Algorithm ◽  
Observation Data ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Content Type ◽  
Small Uav ◽  
Kalman Filter Algorithm

Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

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