Long-Term Visual Tracking of an Unmanned Surface Vehicle in Complex Environment Based on Adaptive Multi-Feature Fusion with Correlation Filtering

2021 ◽  
Author(s):  
Meng Joo Er ◽  
Shiwei Shen ◽  
Yuhong Zheng ◽  
Xun Ji
Keyword(s):  
Visual Tracking ◽  
Feature Fusion ◽  
Complex Environment ◽  
Unmanned Surface Vehicle ◽  
Correlation Filtering

scholarly journals Deeper Siamese network with multi‐level feature fusion for real‐time visual tracking

2019 ◽  
Vol 55 (13) ◽  
pp. 742-745 ◽  
Author(s):  
Kang Yang ◽  
Huihui Song ◽  
Kaihua Zhang ◽  
Jiaqing Fan
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Feature Fusion ◽  
Siamese Network ◽  
Multi Level

Vision-based target point tracking and aiming method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiao Bo Liang ◽  
Xinghua Qu ◽  
YuanJun Zhang ◽  
Lianyin Xu ◽  
Fumin Zhang
Keyword(s):  
Standard Deviation ◽  
Target Tracking ◽  
High Precision ◽  
Visual Tracking ◽  
Laser Ranging ◽  
Content Type ◽  
Mean Error ◽  
Wide Range ◽  
The Mean ◽  
Correlation Filtering

Purpose Laser absolute distance measurement has the characteristics of high precision, wide range and non-contact. In laser ranging system, tracking and aiming measurement point is the precondition of automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach For the central point of the hollow angle coupled mirror, this paper proposes a method based on correlation filtering and ellipse fitting. For non-cooperative target points, this paper proposes an extraction method based on correlation filtering and feature matching. Finally, a visual tracking and aiming system was constructed by combining the two-axis turntable, and experiments were carried out. Findings The target tracking algorithm has an accuracy of 91.15% and a speed of 19.5 frames per second. The algorithm can adapt to the change of target scale and short-term occlusion. The mean error and standard deviation of the center point extraction of the hollow Angle coupling mirror are 0.20 and 0.09 mm. The mean error and standard deviation of feature points matching for non-cooperative target were 0.06 mm and 0.16 mm. The visual tracking and aiming system can track a target running at a speed of 0.7 m/s, aiming error mean is 1.74 pixels and standard deviation is 0.67 pixel. Originality/value The results show that this method can achieve fast and high precision target tracking and aiming and has great application value in laser ranging.

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

2020 ◽  
Author(s):  
Tianyu Xu ◽  
Yongchuan Yu ◽  
Jianzhuo Yan ◽  
Hongxia Xu
Keyword(s):  
Prediction Model ◽  
Feature Fusion ◽  
Forecast Model ◽  
Data Sets ◽  
Good Prediction ◽  
Data Set ◽  
Rainfall Prediction ◽  
Improve Model ◽  
Probability Prediction

Abstract Due to the problems of unbalanced data sets and distribution differences in long-term rainfall prediction, the current rainfall prediction model had poor generalization performance and could not achieve good prediction results in real scenarios. This study uses multiple atmospheric parameters (such as temperature, humidity, atmospheric pressure, etc.) to establish a TabNet-LightGbm rainfall probability prediction model. This research uses feature engineering (such as generating descriptive statistical features, feature fusion) to improve model accuracy, Borderline Smote algorithm to improve data set imbalance, and confrontation verification to improve distribution differences. The experiment uses 5 years of precipitation data from 26 stations in the Beijing-Tianjin-Hebei region of China to verify the proposed rainfall prediction model. The test set is to predict the rainfall of each station in one month. The experimental results shows that the model has good performance with AUC larger than 92%. The method proposed in this study further improves the accuracy of rainfall prediction, and provides a reference for data mining tasks.

Long-Term Object Tracking Based On Feature Fusion

2018 ◽  
Vol 38 (11) ◽  
pp. 1115002
Author(s):  
葛宝义 Ge Baoyi ◽  
左宪章 Zuo Xianzhang ◽  
胡永江 Hu Yongjiang
Keyword(s):  
Object Tracking ◽  
Feature Fusion

Long-term reliable visual tracking with UAVs

2017 ◽  
Author(s):  
Zhenshen Qu ◽  
Xiao Lv ◽  
Junyu Liu ◽  
Li Jiang ◽  
Liang Liang ◽  
...  
Keyword(s):  
Visual Tracking

scholarly journals Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV

2016 ◽  
pp. 557-568 ◽  
Author(s):  
Gustav Häger ◽  
Goutam Bhat ◽  
Martin Danelljan ◽  
Fahad Shahbaz Khan ◽  
Michael Felsberg ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Person Detection

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

Sign in / Sign up

Export Citation Format

Share Document