scholarly journals Visual Object Tracking Based on 2DPCA and ML

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ming-Xin Jiang ◽  
Min Li ◽  
Hong-Yu Wang
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Likelihood Estimation ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Error Matrix ◽  
Template Update ◽  
Update Strategy

We present a novel visual object tracking algorithm based on two-dimensional principal component analysis (2DPCA) and maximum likelihood estimation (MLE). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the model of sparsity constrained MLE is established. Abnormal pixels in the samples will be assigned with low weights to reduce their effects on the tracking algorithm. The object tracking results are obtained by using Bayesian maximum a posteriori (MAP) probability estimation. Finally, to further reduce tracking drift, we employ a template update strategy which combines incremental subspace learning and the error matrix. This strategy adapts the template to the appearance change of the target and reduces the influence of the occluded target template as well. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

scholarly journals Robust Online Object Tracking Based on Feature Grouping and 2DPCA

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Ming-Xin Jiang ◽  
Jun-Xing Zhang ◽  
Min Li
Keyword(s):  
Object Tracking ◽  
Principal Component ◽  
Subspace Learning ◽  
Tracking Algorithm ◽  
Posteriori Probability ◽  
Error Matrix ◽  
A Posteriori Probability ◽  
Template Update ◽  
Feature Grouping ◽  
Update Strategy

We present an online object tracking algorithm based on feature grouping and two-dimensional principal component analysis (2DPCA). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the object templates are grouped into a more discriminative image and a less discriminative image by computing the variance of the pixels in multiple frames. Then, the projection matrix is learned according to the more discriminative image and the less discriminative image, and the samples are projected. The object tracking results are obtained using Bayesian maximum a posteriori probability estimation. Finally, we employ a template update strategy which combines incremental subspace learning and the error matrix to reduce tracking drift. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

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

scholarly journals Discriminative and Robust Online Learning for Siamese Visual Tracking

2020 ◽  
Vol 34 (07) ◽  
pp. 13017-13024 ◽  
Author(s):  
Jinghao Zhou ◽  
Peng Wang ◽  
Haoyang Sun
Keyword(s):  
Discriminative Learning ◽  
Visual Object ◽  
Specific Context ◽  
Visual Object Tracking ◽  
Robust Learning ◽  
Online Module ◽  
Recent Success ◽  
Template Update ◽  
Update Strategy ◽  
Siamese Networks

The problem of visual object tracking has traditionally been handled by variant tracking paradigms, either learning a model of the object's appearance exclusively online or matching the object with the target in an offline-trained embedding space. Despite the recent success, each method agonizes over its intrinsic constraint. The online-only approaches suffer from a lack of generalization of the model they learn thus are inferior in target regression, while the offline-only approaches (e.g., convolutional siamese trackers) lack the target-specific context information thus are not discriminative enough to handle distractors, and robust enough to deformation. Therefore, we propose an online module with an attention mechanism for offline siamese networks to extract target-specific features under L2 error. We further propose a filter update strategy adaptive to treacherous background noises for discriminative learning, and a template update strategy to handle large target deformations for robust learning. Effectiveness can be validated in the consistent improvement over three siamese baselines: SiamFC, SiamRPN++, and SiamMask. Beyond that, our model based on SiamRPN++ obtains the best results over six popular tracking benchmarks and can operate beyond real-time.

Investigation of Threading Building Blocks Framework on Real Time Visual Object Tracking Algorithm

2014 ◽  
Vol 666 ◽  
pp. 240-244
Author(s):  
M.C. Ang ◽  
Elankovan Sundararajan ◽  
K.W. Ng ◽  
Amirhossein Aghamohammadi ◽  
T.L. Lim
Keyword(s):  
Image Processing ◽  
Object Tracking ◽  
Real Time ◽  
Building Blocks ◽  
Search And Rescue ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Core System ◽  
And Robotics

Object tracking plays important roles in various applications such as surveillance, search and rescue, augmented reality and robotics. This paper presents an investigation on multi-threading framework capability for color-based object tracking applications. A multi-threading framework based on Threading Building Blocks (TBB) was implemented on a multi-core system to enhance the image processing performance on a real-time visual object tracking algorithm. Intel Parallel Studio was used to implement this parallel framework. The performance between sequential and multi-threading framework was evaluated and compared. We demonstrated the multi-threading framework was approximately two times faster when compared to the sequential framework in our experiments.

The visual object tracking algorithm research based on adaptive combination kernel

2019 ◽  
Vol 10 (12) ◽  
pp. 4855-4867 ◽  
Author(s):  
Yuantao Chen ◽  
Jin Wang ◽  
Runlong Xia ◽  
Qian Zhang ◽  
Zhouhong Cao ◽  
...  
Keyword(s):  
Object Tracking ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Adaptive Combination
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