Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking

@article{MarvastiZadeh2021EffectiveFO,
  title={Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking},
  author={Seyed Mojtaba Marvasti-Zadeh and Hossein Ghanei-Yakhdan and Shohreh Kasaei and Kamal Nasrollahi and Thomas Baltzer Moeslund},
  journal={ArXiv},
  year={2021},
  volume={abs/2004.01382}
}
Visual object tracking remains an active research field in computer vision due to persisting challenges with various problem-specific factors in real-world scenes. Many existing tracking methods based on discriminative correlation filters (DCFs) employ feature extraction networks (FENs) to model the target appearance during the learning process. However, using deep feature maps extracted from FENs based on different residual neural networks (ResNets) has not previously been investigated. This… Expand
1 Citations

Figures and Tables from this paper

Deep Learning for Visual Tracking: A Comprehensive Survey
TLDR
This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics, and extensively evaluates and analyzes the leading visualtracking methods. Expand

References

SHOWING 1-10 OF 119 REFERENCES
Good Features to Correlate for Visual Tracking
TLDR
Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework, fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap. Expand
Convolutional Features for Correlation Filter Based Visual Tracking
TLDR
The results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers, and show that the convolutional features provide improved results compared to standard hand-crafted features. Expand
Deep feature tracking based on interactive multiple model
TLDR
A novel tracking algorithm based on interactive multiple model ( IMM) framework for better exploring deep features from different layers (IMM_DFT) is proposed and achieves more favorable performance than several state-of-the-art methods. Expand
Visual Tracking with Fully Convolutional Networks
TLDR
An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly. Expand
Learning Dynamic Siamese Network for Visual Object Tracking
TLDR
This paper proposes dynamic Siamese network, via a fast transformation learning model that enables effective online learning of target appearance variation and background suppression from previous frames, and presents elementwise multi-layer fusion to adaptively integrate the network outputs using multi-level deep features. Expand
Online object tracking based on CNN with spatial-temporal saliency guided sampling
TLDR
This work incorporated spatial-temporal saliency detection to guide a more accurate target localization for qualified sampling within an inter-frame motion flow map and has shown a superior performance in comparison to the other state-of-art trackers on both challenging non-rigid and generic tracking benchmark datasets. Expand
WAEF: Weighted Aggregation with Enhancement Filter for Visual Object Tracking
TLDR
This paper proposes a different approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion, and provides a statistics based ensembler approach for integrating the conventionally driven spatial regression results and the proposed temporal regression results to accomplish better tracking. Expand
Multi-cue Correlation Filters for Robust Visual Tracking
TLDR
This paper proposes an efficient multi-cue analysis framework for robust visual tracking by combining different types of features, and constructs multiple experts through Discriminative Correlation Filter and each of them tracks the target independently. Expand
Learning target-aware correlation filters for visual tracking
TLDR
The TACF formulation is proposed, which proposes an optimization strategy based on the Preconditioned Conjugate Gradient method for efficient filter learning and achieves state-of-the-art performance on OTB100 and the top rank in EAO on the VOT2016 challenge. Expand
Deep motion features for visual tracking
TLDR
To the best of the knowledge, this paper is the first to propose fusing appearance information with deep motion features for visual tracking, and it is shown that hand-crafted, deep RGB, and deepmotion features contain complementary information. Expand
...
1
2
3
4
5
...