scholarly journals A Real-Time Structured Output Tracker with Scale Adaption for Visual Target Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kaiyun Yang ◽  
Xuedong Wu ◽  
Jingxiang Xu
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Visual Target ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Output Tracking ◽  
Real Time Tracking ◽  
Structured Output ◽  
Visual Target Tracking

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift

2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Extreme Value ◽  
Tracking Algorithm ◽  
Real Time Tracking ◽  
The Mean ◽  
Particle Filter Algorithm ◽  
Local Extreme

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.

Real-time visual target tracking: two implementations of velocity-based smooth pursuit

1995 ◽  
Author(s):  
Ralph Etienne-Cummings ◽  
Paul Longo ◽  
Jan Van der Spiegel ◽  
Paul Mueller
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Smooth Pursuit ◽  
Visual Target ◽  
Visual Target Tracking

Object Detection and Recognition Algorithm of Moving UAV Based on Machine Vision

2016 ◽  
Vol 13 (10) ◽  
pp. 7731-7737
Author(s):  
Mao Jun
Keyword(s):  
Target Tracking ◽  
Optimal Solution ◽  
Target Position ◽  
Multiple Instance Learning ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
L1 Norm ◽  
Original Distribution ◽  
Local Optimal Solution ◽  
Distribution Field

To conquer disadvantages of slow speed of target tracking algorithm in original distribution field as well as easiness of being caught in local optimal solution, one target tracking algorithm of real-time distribution field based on global matching is presented in the Thesis, thus remarkably improving performance of target tracking algorithm in distribution field. In proposed algorithm, relevant correlation coefficients will be used to substitute the similarity between target distribution filed of original L1 norm measurement and candidate distribution field. As a consequence, the target search process can concert from time domain operation to operation processing of frequency domain among which the latter has lower computation complexity and capability of global search of target position so as to conquer disadvantages such as randomness caused by sparse sampling and that the gradient descent of target tracking algorithm in original distribution field is liable to be caught in local optimal solution. In 12 challenging video sequences, compared with multiple-instance learning and tracking algorithm and tracking algorithm of original distribution field, the method proposed in the Thesis has acquired the optimum performance in tracking accuracy, success rate and speed.

Target tracking based on standard hedging and feature fusion for robot

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sixian Chan ◽  
Jian Tao ◽  
Xiaolong Zhou ◽  
Binghui Wu ◽  
Hongqiang Wang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Visual Target ◽  
Feature Fusion ◽  
Industrial Robots ◽  
Superior Performance ◽  
Human Beings ◽  
Tracking Accuracy ◽  
Content Type ◽  
Feature Optimization ◽  
Visual Target Tracking

Purpose Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion. Design/methodology/approach For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed. Findings Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods. Originality/value Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

Efficient visual target tracking algorithm based on deep spectral convolutional neural networks

2018 ◽  
Vol 47 (6) ◽  
pp. 626005
Author(s):  
郭 强 Guo Qiang ◽  
芦晓红 Lu Xiaohong ◽  
谢英红 Xie Yinghong ◽  
孙 鹏 Sun Peng
Keyword(s):  
Neural Networks ◽  
Target Tracking ◽  
Convolutional Neural Networks ◽  
Visual Target ◽  
Tracking Algorithm ◽  
Visual Target Tracking
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