scholarly journals A Detection and Tracking Algorithm for Resolvable Group with Structural and Formation Changes Using the Gibbs-GLMB Filter

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3384 ◽  
Author(s):  
Xinfeng Ru ◽  
Yudong Chi ◽  
Weifeng Liu
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Tracking Error ◽  
Relevant Information ◽  
The State ◽  
Tracking Algorithm ◽  
Detection And Tracking ◽  
Group Target ◽  
Resolvable Group

In the field of resolvable group target tracking, further study on the structure and formation of group targets is helpful to reduce the tracking error of group bluetargets. In this paper, we propose an algorithm to detect whether the structure or formation state of group targets changes. In this paper, a Gibbs Generalized Labeled Multi-Bernoulli (GLMB) filter is used to obtain the estimation of discernible group target bluestates. After obtaining the state estimation of the group target, we extract relevant information based on the estimation data to judge whether the structure or formation state has changed. Finally, several experiments are carried out to verify the algorithm.

scholarly journals Resolvable Group State Estimation with Maneuver Based on Labeled RFS and Graph Theory

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1307
Author(s):  
Weifeng Liu ◽  
Yudong Chi
Keyword(s):  
Graph Theory ◽  
State Estimation ◽  
Target Tracking ◽  
Finite Sets ◽  
Target Model ◽  
Group Members ◽  
Group Target ◽  
Random Finite Sets ◽  
Resolvable Group

In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.

scholarly journals Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs

2021 ◽  
Vol 11 (18) ◽  
pp. 8434
Author(s):  
Kaipeng Wang ◽  
Zhijun Meng ◽  
Zhe Wu
Keyword(s):  
Deep Learning ◽  
Target Tracking ◽  
Target Detection ◽  
Aerial Photography ◽  
Tracking Algorithm ◽  
Aerial Image ◽  
Small Scale ◽  
Detection And Tracking ◽  
Tracking Algorithms ◽  
Ground Target

Target detection and tracking can be widely used in military and civilian scenarios. Unmanned aerial vehicles (UAVs) have high maneuverability and strong concealment, thus they are very suitable for using as a platform for ground target detection and tracking. Most of the existing target detection and tracking algorithms are aimed at conventional targets. Because of the small scale and the incomplete details of the targets in the aerial image, it is difficult to apply the conventional algorithms to aerial photography from UAVs. This paper proposes a ground target image detection and tracking algorithm applied to UAVs using a revised deep learning technology. Aiming at the characteristics of ground targets in aerial images, target detection algorithms and target tracking algorithms are improved. The target detection algorithm is improved to detect small targets on the ground. The target tracking algorithm is designed to recover the target after the target is lost. The target detection and tracking algorithm is verified on the aerial dataset.

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

2014 ◽  
Vol 513-517 ◽  
pp. 448-452
Author(s):  
Xiu Hua Hu ◽  
Lei Guo ◽  
Hui Hui Li
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Fuzzy Clustering ◽  
Tracking System ◽  
Joint Probability ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Correlation Criterion ◽  
Multi Target Tracking ◽  
Fuzzy Clustering Theory

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

scholarly journals A group target tracking algorithm based on topology

2020 ◽  
Vol 1544 ◽  
pp. 012025
Author(s):  
Yingjing Zhang ◽  
Mingyang Liu ◽  
Xin Liu ◽  
Tianhao Wu
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Group Target

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.

scholarly journals Feature Selection Tracking Algorithm Based on Sparse Representation

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hui-dong Lou ◽  
Wei-guang Li ◽  
Yue-en Hou ◽  
Qing-he Yao ◽  
Guo-qiang Ye ◽  
...  
Keyword(s):  
Feature Selection ◽  
Sparse Representation ◽  
Target Tracking ◽  
Visual Tracking ◽  
Fast Algorithm ◽  
The State ◽  
Tracking Algorithm ◽  
Complex Environment

In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.

Analyzing the State Estimation Software Package Reliability Using the Fault Tree Technology

2018 ◽  
Vol 6 (6) ◽  
pp. 24-34
Author(s):  
Irina N. KOLOSOK ◽  
◽  
Elena S. KORKINA ◽  
Alexandr V. TIKHONOV ◽  
◽  
...  
Keyword(s):  
State Estimation ◽  
Software Package ◽  
Fault Tree ◽  
The State ◽  
Package Reliability

A Robust and Efficient Video Moving Object Detection and Tracking Algorithm

2009 ◽  
Vol 35 (8) ◽  
pp. 1055-1062
Author(s):  
Shao-Hua LIU ◽  
Mao-Jun ZHANG ◽  
Zhi-Hui XIONG ◽  
Wang CHEN
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Efficient Video

Target tracking algorithm of information detection for wireless sensor network

2013 ◽  
Vol 33 (4) ◽  
pp. 939-942
Author(s):  
Xiaoyang DING ◽  
Xiaoyan LI
Keyword(s):  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
Tracking Algorithm ◽  
Wireless Sensor ◽  
Information Detection
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