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

High speed long-term visual object tracking algorithm for real robot systems

2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robot Systems ◽  
Real Robot

Design of UAV Single Object Tracking Algorithm Based on Feature Fusion

2021 ◽  
Author(s):  
Changze Li ◽  
Xiaoxiong Liu ◽  
Xingwang Zhang ◽  
Bin Qin
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Tracking Algorithm ◽  
Single Object

Anti-Occlusion Algorithm for Object Tracking Based on Multi-Feature Fusion

2014 ◽  
Vol 610 ◽  
pp. 393-400
Author(s):  
Jie Cao ◽  
Xuan Liang
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Experimental Results ◽  
Occlusion Detection ◽  
Fusion Algorithm ◽  
Fusion Strategy ◽  
Complex Background ◽  
Complex Scenes

Complex background, especially when the object is similar to the background in color or the target gets blocked, can easily lead to tracking failure. Therefore, a fusion algorithm based on features confidence and similarity was proposed, it can adaptively adjust fusion strategy when occlusion occurs. And this confidence is used among occlusion detection, to overcome the problem of inaccurate occlusion determination when blocked by analogue. The experimental results show that the proposed algorithm is more robust in the case of the cover, but also has good performance under other complex scenes.

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.

Distractor-Aware Object Tracking Based on Multi-Feature Fusion and Scale-Adaption

2017 ◽  
Vol 37 (5) ◽  
pp. 0515005
Author(s):  
李双双 Li Shuangshuang ◽  
赵高鹏 Zhao Gaopeng ◽  
王建宇 Wang Jianyu
Keyword(s):  
Object Tracking ◽  
Feature Fusion

SETh: The Method for Long-Term Object Tracking

2014 ◽  
pp. 302-315 ◽  
Author(s):  
Karol Jedrasiak ◽  
Mariusz Andrzejczak ◽  
Aleksander Nawrat
Keyword(s):  
Object Tracking
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