scholarly journals An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking

2006 ◽  
Author(s):  
A.O. Balan ◽  
M.J. Black
Keyword(s):  
Object Tracking ◽  
Appearance Model ◽  
Model Based ◽  
Articulated Object Tracking ◽  
Model Approach

scholarly journals Self-balance Motion and Appearance Model for Multi-object Tracking in UAV

2019 ◽  
Author(s):  
Hongyang Yu ◽  
Guorong Li ◽  
Weigang Zhang ◽  
Hongxun Yao ◽  
Qingming Huang
Keyword(s):  
Object Tracking ◽  
Appearance Model

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.

Dual-scale structural local sparse appearance model for robust object tracking

2017 ◽  
Vol 237 ◽  
pp. 101-113 ◽  
Author(s):  
Zhiqiang Zhao ◽  
Ping Feng ◽  
Tianjiang Wang ◽  
Fang Liu ◽  
Caihong Yuan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Appearance Model ◽  
Dual Scale

Testing Colour Appearance Model Based UCS Using HDR, WCG and COMBVD Datasets

2021 ◽  
Vol 2021 (29) ◽  
pp. 381-386
Author(s):  
Xu Qiang ◽  
Muhammad Safdar ◽  
Ming Ronnier Luo
Keyword(s):  
Correlation Coefficient ◽  
Power Factor ◽  
Marked Improvement ◽  
Appearance Model ◽  
Colour Difference ◽  
Similar Performance ◽  
Model Based ◽  
Test Models ◽  
Appearance Models ◽  
Colour Appearance

Two colour appearance models based UCSs, CAM16-UCS and ZCAM-QMh, were tested using HDR, WCG and COMBVD datasets. As a comparison, two widely used UCSs, CIELAB and ICTCP, were tested. Metrics of the STRESS and correlation coefficient between predicted colour differences and visual differences, together with local and global uniformity based on their chromatic discrimination ellipses, were applied to test models' performance. The two UCSs give similar performance. The luminance parametric factor kL, and power factor γ, were introduced to optimize colour-difference models. Factors kL and γ of 0.75 and 0.5, gave marked improvement to predict the HDR dataset. Factor kL of 0.3 gave significant improvement in the test of WCG dataset. In the test of COMBVD dataset, optimization provide very limited improvement.

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