MSST-ResNet: Deep multi-scale spatiotemporal features for robust visual object tracking

2019 ◽  
Vol 164 ◽  
pp. 235-252 ◽  
Author(s):  
Bing Liu ◽  
Qiao Liu ◽  
Zhengyu Zhu ◽  
Taiping Zhang ◽  
Yong Yang
Keyword(s):  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Spatiotemporal Features ◽  
Multi Scale

Robust real-time visual object tracking via multi-scale fully convolutional Siamese networks

2018 ◽  
Vol 77 (17) ◽  
pp. 22131-22143 ◽  
Author(s):  
Longchao Yang ◽  
Peilin Jiang ◽  
Fei Wang ◽  
Xuan Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Multi Scale ◽  
Siamese Networks

scholarly journals STResNet_CF Tracker: The Deep Spatiotemporal Features Learning for Correlation Filter Based Robust Visual Object Tracking

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30142-30156 ◽  
Author(s):  
Zhengyu Zhu ◽  
Bing Liu ◽  
Yunbo Rao ◽  
Qiao Liu ◽  
Rui Zhang
Keyword(s):  
Object Tracking ◽  
Correlation Filter ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Spatiotemporal Features

Using Segmentation with Multi-scale Selective Kernel for Visual Object Tracking

2022 ◽  
pp. 1-1
Author(s):  
Feng Bao ◽  
Yifei Cao ◽  
Shunli Zhang ◽  
Beibei Lin ◽  
Sicong Zhao
Keyword(s):  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Multi Scale

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.

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
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