Towards Real-Time Visual Tracking with Graded Color-names Features

@article{Li2022TowardsRV,
  title={Towards Real-Time Visual Tracking with Graded Color-names Features},
  author={Lin Li and Guoli Wang and Xuemei Guo},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.08701}
}
MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking… 

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References

SHOWING 1-10 OF 25 REFERENCES

Adaptive Color Attributes for Real-Time Visual Tracking

The contribution of color in a tracking-by-detection framework is investigated and an adaptive low-dimensional variant of color attributes is proposed, suggesting that color attributes provides superior performance for visual tracking.

Kernel-Based Object Tracking

A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.

Long-term correlation tracking

This paper decomposes the task of tracking into translation and scale estimation of objects and shows that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change.

Distribution fields for tracking

A method for building an image descriptor using distribution fields (DFs), a representation that allows smoothing the objective function without destroying information about pixel values, and experimental evidence on the superiority of the width of the basin of attraction around the global optimum of DFs over other descriptors are presented.

A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications

This work proposes an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science, that can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, and achieves robust detection for different types of videos taken with stationary cameras.

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Efficiency figures show that the proposed technique for motion detection outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate.

Real time robust L1 tracker using accelerated proximal gradient approach

This paper proposes an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers and a very fast numerical solver is developed to solve the resulting ℓ1 norm related minimization problem with guaranteed quadratic convergence.

Real Time Foreground-Background Segmentation Using a Modified Codebook Model

An evaluation method is proposed in order to objectively compare several segmentation techniques, based on receiver operating characteristic (ROC) analysis and on precision and recall method, to summarize the quality factor of a method by a single value based on a weighted Euclidean distance or on a harmonic mean between two related characteristics.

Struck: Structured Output Tracking with Kernels

A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance.

Discriminative Scale Space Tracking

This paper proposes a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation in a tracking-by-detection framework that obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state of theart tracker on VOT2014.