scholarly journals Control of the non-stationary gyroscopic system in the target tracking process

2018 ◽  
Keyword(s):  
Target Tracking ◽  
Gyroscopic System ◽  
Tracking Process

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

scholarly journals Demanded Dwell Time Reduction Based on Prior RCS Fluctuation Constraint in Target Tracking

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zijian Wu ◽  
Fei Wang ◽  
Jianjiang Zhou
Keyword(s):  
Target Tracking ◽  
Dwell Time ◽  
Search Algorithm ◽  
Prediction Method ◽  
Airborne Radar ◽  
Multiple Model ◽  
Frequency Method ◽  
Tracking Process ◽  
Fluctuation Range ◽  
Probability Prediction

In the field of airborne radar resource management, one of the most accessible solutions to enhance the power of modern radar system is to save target tracking time as much as possible. During the target tracking process, it is effective to utilize the prior knowledge of the radar cross section (RCS) of the target to save airborne radar resource. Since the target RCS is sensitive to frequency and attitude angle, it is difficult to predict the target RCS accurately. This paper proposes an effective way to save airborne radar time resource in the target tracking process by changing radiation frequency of airborne radar to tolerate certain RCS fluctuation. Firstly, based on the tolerable fluctuation range of the target RCS, this paper designs an effective search algorithm to minimize the frequency set while maximizing the average RCS within the limited fluctuation range. Secondly, the detection probability prediction phase is renewed by taking the RCS fluctuation into account in order to reduce the radar dwell time. Finally, by using interactive multiple model Kalman filter (IMMKF) algorithm, a target tracking procedure with the minimum dwell time prediction method is proposed. Simulation results show that the proposed method is effective. As for target tracking simulation of three different trajectories, the proposed method can save at best 81.35% more dwell time than the fixed frequency method.

Improved target-tracking process in PCL

2017 ◽  
Author(s):  
Jaeuk Baek ◽  
Doyeon Kim ◽  
Hongsuk Shim ◽  
Youngnam Han
Keyword(s):  
Target Tracking ◽  
Tracking Process

scholarly journals The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Support Vector Machine ◽  
Target Tracking ◽  
Visual Saliency ◽  
Image Features ◽  
Tracking Algorithm ◽  
Support Vector ◽  
Intelligent Robots ◽  
Tracking Process ◽  
Adaptive Support ◽  
Novel Target

The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target’s saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.

scholarly journals Scale-Adaptive Context-Aware Correlation Filter with Output Constraints for Visual Target Tracking

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jingxiang Xu ◽  
Xuedong Wu ◽  
Zhiyu Zhu ◽  
Kaiyun Yang ◽  
Yanchao Chang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Correlation Filter ◽  
Context Aware ◽  
Tracking Accuracy ◽  
Large Area ◽  
Posterior Probability Distribution ◽  
Output Response ◽  
Tracking Process ◽  
Output Constraints ◽  
Tracking Model

Context-aware correlation filter tracker is one of the most advanced target trackers, and it has significant improvement in tracking accuracy and success rate compared with traditional trackers. However, because the complexity of background in the process of tracking can lead to inaccurate output response of target tracking, an accurate tracking model is difficult to be established. Moreover, the drift problem is easy to occur during the tracking process due to the imprecise tracking model, especially when the target has large area occlusion, fast motion, and deformation. Aiming at the drift problem in the target tracking process, a novel algorithm is proposed in this paper. The developed method derives the specific representation of constraint output by assuming that the output response is Gaussian distribution, and a variable update parameter is obtained based on the output constraint relationship at first, then the tracking filter is selectively updated with changeable update parameters and fixed update parameters, and finally, the target scale is updated with maximizing posterior probability distribution. The effectiveness of developed algorithm is verified by comparing with other trackers on OTB-50 and OTB-100 evaluation benchmark datasets, and the experimental results have shown that the suggested tracker has higher overall object tracking performance than other trackers.

A Survey of Robot Visual Tracking Algorithm

2012 ◽  
Vol 501 ◽  
pp. 577-582 ◽  
Author(s):  
Yi Hu Huang ◽  
Man Hu ◽  
Hong Lei Chong ◽  
Xi Mei Jia ◽  
Ji Xiang Ma ◽  
...  
Keyword(s):  
Target Tracking ◽  
Visual Tracking ◽  
Robot Vision ◽  
Tracking Algorithm ◽  
Block Rotation ◽  
Vision Systems ◽  
Tracking Process

In this paper, the robot vision systems are studied. Through the analysis of the visual tracking process, this paper classifies and introduces several commonly track. The features affecting the quality of target tracking, such as block, rotation, translation deformation and others, are analyzed and discussed. At last, some further directions of target tracking algorithm are also shortly addressed.

Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion

2015 ◽  
Vol 734 ◽  
pp. 476-481
Author(s):  
Ming Hua Liu ◽  
Chuan Sheng Wang ◽  
Xian Lun Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Dynamic Change ◽  
Tracking Algorithm ◽  
Complex Environments ◽  
Complex Scene ◽  
Features Fusion ◽  
Single Feature ◽  
Tracking Process ◽  
Sift Features

Aiming at the poor robustness problem of using single feature in the target tracking process, a novel tracking algorithm based on color and SIFT features fusion in particle filter framework is presented in complex environments. Color and SIFT features are selected to establish the target model according to their stability, The scale and rotation invariance of SIFT feature and resistance occlusion property of color feature has been fused in the particle filter framework adaptively. According to the dynamic change of the tracking scene, the fusion weights is updated adaptively. Experimental results show the proposed method can track target robustly under complex scene in real-time performance.

Object Update-Based Mean Shift Tracing Algorithm Combined with LBP

2014 ◽  
Vol 548-549 ◽  
pp. 1185-1191
Author(s):  
Mou Zhong Liu ◽  
Min Sun ◽  
Ya Fen Wang
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Local Binary Pattern ◽  
Mean Shift ◽  
Experimental Result ◽  
Human Face ◽  
Target Model ◽  
Fast Acquisition ◽  
Tracking Process ◽  
Tracing Algorithm

This paper proposed a novel solution to track human face obscured largely in an image on the basis of Mean Shift Tracing Algorithm (MSTA). The improved approach aims to update the target model in real-time during the whole tracking process to avoid target losing. Local Binary Pattern (LBP) theory is chosen to improve the original MSTA here. The experimental result shows that our new solution has a better performance in target tracking under situations like face rotation and occlusion as well as in fast acquisition when faces reappear on the screen.

scholarly journals The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Cundong Tang ◽  
Li Chen ◽  
Yi Wang ◽  
Wusi Yang ◽  
Rui Chen ◽  
...  
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Practical Significance ◽  
Support Vector ◽  
Detector Performance ◽  
Real Time System ◽  
Target Model ◽  
Tracking Process ◽  
5G Network

With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

scholarly journals Multi-target tracking algorithm in intelligent transportation based on wireless sensor network

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1000-1008 ◽  
Author(s):  
Yang Lei ◽  
Yuan Wu ◽  
Ahmad Jalal Khan Chowdhury
Keyword(s):  
Target Tracking ◽  
Information Exchange ◽  
Intelligent Transportation System ◽  
Tracking Error ◽  
Intelligent Transportation ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Multiple Targets ◽  
Tracking Process ◽  
Multi Target Tracking

Abstract The traditional extended Kalman algorithm for multi-target tracking in the field of intelligent transportation does not consider the occlusion problem of the multi-target tracking process, and has the disadvantage of low multi-target tracking accuracy. A multi-target tracking algorithm using wireless sensors in an intelligent transportation system is proposed. Based on the dynamic clustering structure, the measurement results of each sensor are the superimposed results of sound signals and environmental noise from multiple targets. During the tracking process, each target corresponds to a particle filter. When the target spacing is relatively close to each other, each master node realizes distributed multi-target tracking through information exchange. At the same time, it is also necessary to consider the overlap between adjacent frames. Since the moving target speed is too fast, the target occlusion has the least influence on the tracking accuracy, and can accurately track multiple targets. The experimental results show that the proposed algorithm has a target tracking error of 0.5 m to 1 m, and the tracking result has high precision.

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