scholarly journals Feature Integration with Adaptive Importance Maps for Visual Tracking

2018 ◽  
Author(s):  
Aishi Li ◽  
Ming Yang ◽  
Wanqi Yang
Keyword(s):  
State Of The Art ◽  
Feature Integration ◽  
Circulant Matrices ◽  
Visual Object ◽  
Correlation Filters ◽  
Excellent Performance ◽  
Complementary Information ◽  
Visual Object Tracking ◽  
Penalty Factor ◽  
Dense Sampling

Discriminative correlation filters have recently achieved excellent performance for visual object tracking. The key to success is to make full use of dense sampling and specific properties of circulant matrices in the Fourier domain. However, previous studies don't take into consideration the importance and complementary information of different features, simply concatenating them. This paper investigates an effective method of feature integration for correlation filters, which jointly learns filters, as well as importance maps in each frame. These importance maps borrow the advantages of different features, aiming to achieve complementary traits and improve robustness. Moreover, for each feature, an importance map is shared by its all channels to avoid overfitting. In addition, we introduce a regularization term for the importance maps and use the penalty factor to control the significance of features. Based on handcrafted and CNN features, we implement two trackers, which achieve a competitive performance compared with several state-of-the-art trackers.

scholarly journals Visual Object Tracking Robust to Illumination Variation Based on Hyperline Clustering

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
Senquan Yang ◽  
Yuan Xie ◽  
Pu Li ◽  
Haoxiang Wen ◽  
Huan Luo ◽  
...  
Keyword(s):  
State Of The Art ◽  
Shape Deformation ◽  
Visual Object ◽  
Excellent Performance ◽  
Illumination Variation ◽  
Visual Object Tracking ◽  
Discriminant Model ◽  
Lower Accuracy ◽  
Benchmark Datasets ◽  
Online Tracking

Color histogram-based trackers have obtained excellent performance against many challenging situations. However, since the appearance of color is sensitive to illumination, they tend to achieve lower accuracy when illumination is severely variant throughout a sequence. To overcome this limitation, we propose a novel hyperline clustering based discriminant model, an illumination invariant model that is able to distinguish the object from its surrounding background. Furthermore, we exploit this model and propose an anchor based scale estimation to cope with shape deformation and scale variation. Numerous experiments on recent online tracking benchmark datasets demonstrate that our approach achieve favorable performance compared with several state-of-the-art tracking algorithms. In particular, our approach achieves higher accuracy than comparative methods in the illumination variant and shape deformation challenging situations.

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.

scholarly journals Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4021 ◽  
Author(s):  
Mustansar Fiaz ◽  
Arif Mahmood ◽  
Soon Ki Jung
Keyword(s):  
Object Tracking ◽  
Spatial Attention ◽  
Feature Fusion ◽  
State Of The Art ◽  
Feature Representation ◽  
Visual Object ◽  
Target Feature ◽  
Visual Object Tracking ◽  
Low Level ◽  
Benchmark Datasets

We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers.

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
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