Motion-Based Feature Selection and Adaptive Template Update Strategy for Robust Visual Tracking

2016 ◽  
Author(s):  
Baofeng Wang ◽  
Zhiquan Qi ◽  
Sizhong Chen
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Template Update ◽  
Update Strategy

scholarly journals An Improved Kernelized Correlation Filter Based Visual Tracking Method

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianjun Ni ◽  
Xue Zhang ◽  
Pengfei Shi ◽  
Jinxiu Zhu
Keyword(s):  
Visual Tracking ◽  
Template Matching ◽  
Visual Target ◽  
State Of The Art ◽  
Correlation Filter ◽  
Matching Method ◽  
Tracking Method ◽  
Template Update ◽  
Update Strategy ◽  
Visual Target Tracking

Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

scholarly journals A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 162668-162682
Author(s):  
Qingsong Xie ◽  
Kewei Liu ◽  
An Zhiyong ◽  
Lei Wang ◽  
Ye Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Template Update ◽  
Update Strategy

Adaptive Feature Selection Siamese Networks for Visual Tracking

2020 ◽  
pp. 167-179
Author(s):  
Mustansar Fiaz ◽  
Md. Maklachur Rahman ◽  
Arif Mahmood ◽  
Sehar Shahzad Farooq ◽  
Ki Yeol Baek ◽  
...  
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Adaptive Feature Selection ◽  
Siamese Networks

Effective template update mechanism in visual tracking with background clutter

2020 ◽  
Author(s):  
Shuai Liu ◽  
Dongye Liu ◽  
Khan Muhammad ◽  
Weiping Ding
Keyword(s):  
Visual Tracking ◽  
Template Update ◽  
Background Clutter

Visual tracking algorithm based on improved Bhattacharyya coefficient and model update strategy

2015 ◽  
Vol 36 (1) ◽  
pp. 52-57
Author(s):  
Huang An-qi ◽  
◽  
Hou Zhi-qiang ◽  
Yu Wang-sheng ◽  
Liu Xiang
Keyword(s):  
Visual Tracking ◽  
Tracking Algorithm ◽  
Bhattacharyya Coefficient ◽  
Model Update ◽  
Update Strategy

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.

scholarly journals Incremental learning-based visual tracking with weighted discriminative dictionaries

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989015
Author(s):  
Penggen Zheng ◽  
Huimin Zhao ◽  
Jin Zhan ◽  
Yijun Yan ◽  
Jinchang Ren ◽  
...  
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Texture Features ◽  
Reconstruction Error ◽  
Selection Strategy ◽  
Position Information ◽  
Color Features ◽  
Training Samples ◽  
Noise Energy ◽  
Sparse Feature Selection

Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.

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.

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