scholarly journals Motion-Aware Correlation Filters for Online Visual Tracking

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3937 ◽  
Author(s):  
Yihong Zhang ◽  
Yijin Yang ◽  
Wuneng Zhou ◽  
Lifeng Shi ◽  
Demin Li
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Model Updating ◽  
Visual Object ◽  
Adaptive Model ◽  
Correlation Filters ◽  
Current Frame ◽  
Visual Object Tracking ◽  
Fast Motion ◽  
Better Than

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

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 Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters

2021 ◽  
Author(s):  
Tianyang Xu ◽  
Zhenhua Feng ◽  
Xiao-Jun Wu ◽  
Josef Kittler
Keyword(s):  
Object Tracking ◽  
Augmented Lagrangian Method ◽  
Channel Selection ◽  
Image Feature ◽  
Superior Performance ◽  
Appearance Model ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking ◽  
Feature Representations

AbstractDiscriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.

Visual object tracking by correlation filters and online learning

2018 ◽  
Vol 140 ◽  
pp. 77-89 ◽  
Author(s):  
Xin Zhang ◽  
Gui-Song Xia ◽  
Qikai Lu ◽  
Weiming Shen ◽  
Liangpei Zhang
Keyword(s):  
Online Learning ◽  
Object Tracking ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking

scholarly journals Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking

2019 ◽  
pp. 646-661
Author(s):  
Litu Rout ◽  
Priya Mariam Raju ◽  
Deepak Mishra ◽  
Rama Krishna Sai Subrahmanyam Gorthi
Keyword(s):  
Object Tracking ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking

scholarly journals Parallel Correlation Filters for Real-Time Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2362 ◽  
Author(s):  
Yijin Yang ◽  
Yihong Zhang ◽  
Demin Li ◽  
Zhijie Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Research Field ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Visual Object ◽  
Correlation Filters ◽  
Illumination Variation ◽  
Visual Object Tracking ◽  
Appearance Changes

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.

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