Visual Target Tracking via Online Reliability Evaluation and Feature Selection in the Framework of Correlation Filtering

2020 ◽  
Vol 13 (7) ◽  
pp. 1068-1077
Author(s):  
Li Wei ◽  
Meng Ding ◽  
Yun-Feng Cao ◽  
Xu Zhang
Keyword(s):  
Feature Selection ◽  
Visual Tracking ◽  
Reliability Evaluation ◽  
Correlation Filter ◽  
Selection Scheme ◽  
Discriminant Ability ◽  
Visual Target Tracking ◽  
Assessment Result ◽  
Precision Rate ◽  
Correlation Filtering

Background: Although correlation filtering is one of the most successful visual tracking frameworks, it is prone to drift caused by several factors such as occlusion, deformation and rotation. Objective: In order to improve the performance of correlation filter-based trackers, this paper proposes a visual tracking method via online reliability evaluation and feature selection. Methods: The main contribution of this paper is to introduce three schemes in the framework of correlation filtering. Firstly, we present an online reliability evaluation to assess the current tracking result by using the method of adaptive threshold segmentation of response map. Secondly, the proposed tracker updates the regression model of correlation filter according to the assessment result. Thirdly, when the tracking result based on a handcrafted feature is not reliable enough, we propose a feature selection scheme that autonomously replaces a handcrafted feature used in the traditional correlation filter-based trackers with a deep convolutional feature that can re-capture the target by its powerful discriminant ability. Results: On OTB-2013datasets, the Precision rate and Success rate of the proposed tracking algorithm can reach 84.8% and 62.5%, respectively. Moreover, the tracking speed of proposed algorithm is 19 frame per second. Conclusion: The quantitative and qualitative experimental results both demonstrate that the proposed algorithm performed favorably against nine state-of-the-art algorithms.

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.

Learning spatio-temporal correlation filter for visual tracking

2021 ◽  
Vol 436 ◽  
pp. 273-282
Author(s):  
Youmin Yan ◽  
Xixian Guo ◽  
Jin Tang ◽  
Chenglong Li ◽  
Xin Wang
Keyword(s):  
Visual Tracking ◽  
Temporal Correlation ◽  
Correlation Filter ◽  
Spatio Temporal

scholarly journals An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3627
Author(s):  
Bo Jin ◽  
Chunling Fu ◽  
Yong Jin ◽  
Wei Yang ◽  
Shengbin Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Structure ◽  
Gene Selection ◽  
Dimensional Space ◽  
Original Data ◽  
Global Structure ◽  
Biological Data ◽  
Special Treatment ◽  
Selection Scheme ◽  
Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

scholarly journals VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 975-982 ◽  
Author(s):  
A. Makedonas ◽  
C. Theoharatos ◽  
V. Tsagaris ◽  
V. Anastasopoulos ◽  
S. Costicoglou
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Selection Procedure ◽  
Texture Features ◽  
Statistical Characteristics ◽  
Selection Scheme ◽  
Feature Extraction And Selection ◽  
Sar Data ◽  
Ship Classification ◽  
Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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