scholarly journals Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3513 ◽  
Author(s):  
Gang-Joon Yoon ◽  
Hyeong Hwang ◽  
Sang Yoon
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Intelligent Systems ◽  
Principal Component ◽  
Research Area ◽  
Target Object ◽  
Visual Object ◽  
Appearance Modeling ◽  
Sparse Principal Component Analysis ◽  
Visual Object Tracking

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences.

Visual Object Tracking Using Fast Three-Dimensional Discrete Cosine Transform

2014 ◽  
Vol 602-605 ◽  
pp. 1689-1692
Author(s):  
Cong Lin ◽  
Chi Man Pun
Keyword(s):  
Object Tracking ◽  
Discrete Cosine Transform ◽  
Three Dimensional ◽  
Video Stream ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Cosine Transform ◽  
Color Video ◽  
Real Time Application

A novel visual object tracking method for color video stream based on traditional particle filter is proposed in this paper. Feature vectors are extracted from coefficient matrices of fast three-dimensional Discrete Cosine Transform (fast 3-D DCT). The feature, as experiment showed, is very robust to occlusion and rotation and it is not sensitive to scale changes. The proposed method is efficient enough to be used in a real-time application. The experiment was carried out on some common used datasets in literature. The results are satisfied and showed the estimated trace follows the target object very closely.

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 Visual Object Tracking using Sparse Representation and Interest Points in a Double Step Approach

2020 ◽  
Vol 175 (10) ◽  
pp. 1-9
Author(s):  
Mohamad Hosein Davoodabadi Farahani ◽  
Mohsen Khan Mohamadi ◽  
Mojtaba Lotfizad
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Visual Object ◽  
Interest Points ◽  
Double Step ◽  
Visual Object Tracking

Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)

2011 ◽  
Vol 44 (9) ◽  
pp. 2170-2183 ◽  
Author(s):  
Zhenjun Han ◽  
Jianbin Jiao ◽  
Baochang Zhang ◽  
Qixiang Ye ◽  
Jianzhuang Liu
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

scholarly journals Distractor-Aware Deep Regression for Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 387 ◽  
Author(s):  
Ming Du ◽  
Yan Ding ◽  
Xiuyun Meng ◽  
Hua-Liang Wei ◽  
Yifan Zhao
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Test Data ◽  
Loss Function ◽  
State Of The Art ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Better Than

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

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