scholarly journals Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 1963
Author(s):  
Shanshan Luo ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Huawei Liu
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Evaluation Method ◽  
Low Rank ◽  
Sparse Learning ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
The Impact

The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.

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 Improved Hierarchical Convolutional Features for Robust Visual Object Tracking

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinping Sun
Keyword(s):  
Object Tracking ◽  
Target Position ◽  
Feature Representation ◽  
Correlation Filter ◽  
Low Rank ◽  
Visual Object ◽  
Threshold Condition ◽  
Current Frame ◽  
Visual Object Tracking

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.

scholarly journals Low-Rank Multi-Channel Features for Robust Visual Object Tracking

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1155 ◽  
Author(s):  
Fawad ◽  
Muhammad Jamil Khan ◽  
MuhibUr Rahman ◽  
Yasar Amin ◽  
Hannu Tenhunen
Keyword(s):  
Computational Complexity ◽  
Object Tracking ◽  
State Of The Art ◽  
Color Naming ◽  
Circulant Matrix ◽  
Low Rank ◽  
Support Vector ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Kernel Correlation

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

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.

scholarly journals ACSiamRPN: Adaptive Context Sampling for Visual Object Tracking

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1528
Author(s):  
Xiaofei Qin ◽  
Yipeng Zhang ◽  
Hang Chang ◽  
Hao Lu ◽  
Xuedian Zhang
Keyword(s):  
Object Tracking ◽  
Long Range ◽  
Global Maximum ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Siamese Network ◽  
Information Embedding ◽  
Selection Step ◽  
Block Based ◽  
The Impact

In visual object tracking fields, the Siamese network tracker, based on the region proposal network (SiamRPN), has achieved promising tracking effects, both in speed and accuracy. However, it did not consider the relationship and differences between the long-range context information of various objects. In this paper, we add a global context block (GC block), which is lightweight and can effectively model long-range dependency, to the Siamese network part of SiamRPN so that the object tracker can better understand the tracking scene. At the same time, we propose a novel convolution module, called a cropping-inside selective kernel block (CiSK block), based on selective kernel convolution (SK convolution, a module proposed in selective kernel networks) and use it in the region proposal network (RPN) part of SiamRPN, which can adaptively adjust the size of the receptive field for different types of objects. We make two improvements to SK convolution in the CiSK block. The first improvement is that in the fusion step of SK convolution, we use both global average pooling (GAP) and global maximum pooling (GMP) to enhance global information embedding. The second improvement is that after the selection step of SK convolution, we crop out the outermost pixels of features to reduce the impact of padding operations. The experiment results show that on the OTB100 benchmark, we achieved an accuracy of 0.857 and a success rate of 0.643. On the VOT2016 and VOT2019 benchmarks, we achieved expected average overlap (EAO) scores of 0.394 and 0.240, respectively.

Re-identification framework for long term visual object tracking based on object detection and classification

2020 ◽  
Vol 88 ◽  
pp. 115969 ◽  
Author(s):  
Paraskevi Nousi ◽  
Danai Triantafyllidou ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

scholarly journals Re2EMA: Regularized and Reinitialized Exponential Moving Average for Target Model Update in Object Tracking

2019 ◽  
Vol 33 ◽  
pp. 8457-8464
Author(s):  
Jianglei Huang ◽  
Wengang Zhou
Keyword(s):  
Object Tracking ◽  
Moving Average ◽  
Transformation Matrix ◽  
Visual Object ◽  
Optimal Model ◽  
Visual Object Tracking ◽  
Target Model ◽  
Model Update ◽  
Optimal Target

Target model update plays an important role in visual object tracking. However, performing optimal model update is challenging. In this work, we propose to achieve an optimal target model by learning a transformation matrix from the last target model to the newly generated one, which results into a minimization objective. In this objective, there exists two challenges. The first is that the newly generated target model is unreliable. To overcome this problem, we propose to impose a penalty to limit the distance between the learned target model and the last one. The second is that as time evolves, we can not decide whether the last target model has been corrupted or not. To get out of this dilemma, we propose a reinitialization term. Besides, to control the complexity of the transformation matrix, we also add a regularizer. We find that the optimization formula’s solution, with some simplifications, degenerates to EMA. Finally, despite the simplicity, extensive experiments conducted on several commonly used benchmarks demonstrate the effectiveness of our proposed approach in relatively long term scenarios.

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