scholarly journals Alleviate Similar Object in Visual Tracking via Online Learning Interference-Target Spatial Structure

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2382 ◽  
Author(s):  
Guokai Shi ◽  
Tingfa Xu ◽  
Jiqiang Luo ◽  
Jie Guo ◽  
Zishu Zhao
Keyword(s):  
Online Learning ◽  
Spatial Structure ◽  
Visual Tracking ◽  
Similar Object ◽  
Learning Interference

scholarly journals Adaptive Online Learning Based Robust Visual Tracking

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 14790-14798 ◽  
Author(s):  
Weiming Yang ◽  
Meirong Zhao ◽  
Yinguo Huang ◽  
Yelong Zheng
Keyword(s):  
Online Learning ◽  
Visual Tracking ◽  
Adaptive Online Learning

scholarly journals Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Spatial Structure ◽  
Particle Filter ◽  
Visual Tracking ◽  
Color Image ◽  
Structural Information ◽  
Image Sequences ◽  
Histogram Of Oriented Gradient ◽  
Image Patches ◽  
Gradient Orientation ◽  
Comparison Algorithms

To overcome the problem of occlusion in visual tracking, this paper proposes an occlusion-aware tracking algorithm. The proposed algorithm divides the object into discrete image patches according to the pixel distribution of the object by means of clustering. To avoid the drifting of the tracker to false targets, the proposed algorithm extracts the dominant features, such as color histogram or histogram of oriented gradient orientation, from these image patches, and uses them as cues for tracking. To enhance the robustness of the tracker, the proposed algorithm employs an implicit spatial structure between these patches as another cue for tracking; Afterwards, the proposed algorithm incorporates these components into the particle filter framework, which results in a robust and precise tracker. Experimental results on color image sequences with different resolutions show that the proposed tracker outperforms the comparison algorithms on handling occlusion in visual tracking

Confidence Based Hierarchical Online Ensemble Visual Tracking

2014 ◽  
Vol 945-949 ◽  
pp. 1794-1800
Author(s):  
Wen Hua He ◽  
Zhi Jing Liu ◽  
Jian Ming Qu
Keyword(s):  
Online Learning ◽  
Visual Tracking ◽  
Adaptive Methods ◽  
Feature Model ◽  
Template Match ◽  
Learning Classifier ◽  
Target Template ◽  
Online Tracking ◽  
The Stability ◽  
The Individual

In order to adapt to the target appearance changes during visual tracking, feature model needs to be updated by online learning. However, online adaptive methods suffer from the drifting problem: error data are used for updating and will finally lead to tracking failure. In this paper, we propose a novel hierarchical online ensemble tracking method. Optical flow tracker is employed to predict the rough location. Online learning classifier is employed to detect the target. Template match is used to filter the data for updating. All these parts are combined together hierarchically by their confidence to ensure the stability of online learning and tracking. In contrast to the individual online learning and semi-supervised online learning, our method show more adaptability and stability. We demonstrate the performance on several public video sequences, which shows the improvement of our method over other online tracking approaches.

scholarly journals TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking

2020 ◽  
Author(s):  
Siyuan Li ◽  
Zhi Zhang ◽  
Ziyu Liu ◽  
Anna Wang ◽  
Linglong Qiu ◽  
...  
Keyword(s):  
Online Learning ◽  
Visual Tracking ◽  
State Of The Art ◽  
Target Localization ◽  
High Quality ◽  
Two Stage ◽  
Joint Learning ◽  
Improve Accuracy ◽  
Multi Level ◽  
Two Stages

Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly. Specifically, our model performs a novel deformable central correlation operation by an online learning model in both two stages to locate new target centers while generating target proposals in the vicinity of these centers. The proposals are refined in the refinement stage to further improve accuracy and robustness. Moreover, the model benefits from multi-level features aggregation in a neck module and a feature enhancement module. We conduct extensive ablation studies to demonstrate the effectiveness of our proposed methods. Our tracker runs at over 30 FPS and sets a new state-of-the-art on five tracking benchmarks, including LaSOT, VOT2018, TrackingNet, GOT10k, OTB2015.

Online learning and joint optimization of combined spatial-temporal models for robust visual tracking

2017 ◽  
Vol 226 ◽  
pp. 221-237 ◽  
Author(s):  
Tao Zhou ◽  
Harish Bhaskar ◽  
Fanghui Liu ◽  
Jie Yang ◽  
Ping Cai
Keyword(s):  
Online Learning ◽  
Visual Tracking ◽  
Joint Optimization ◽  
Temporal Models

Robust visual tracking based on online learning of joint sparse dictionary

2013 ◽  
Author(s):  
Qiaozhe Li ◽  
Yu Qiao ◽  
Jie Yang ◽  
Li Bai
Keyword(s):  
Online Learning ◽  
Visual Tracking
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