scholarly journals Real-Time Visual Tracking with Variational Structure Attention Network

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4904 ◽  
Author(s):  
Yeongbin Kim ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Online Training ◽  
Correlation Filter ◽  
Shape Distortion ◽  
Correlation Filters ◽  
Attention Network ◽  
Real Time Processing ◽  
Variational Structure

Online training framework based on discriminative correlation filters for visual tracking has recently shown significant improvement in both accuracy and speed. However, correlation filter-base discriminative approaches have a common problem of tracking performance degradation when the local structure of a target is distorted by the boundary effect problem. The shape distortion of the target is mainly caused by the circulant structure in the Fourier domain processing, and it makes the correlation filter learn distorted training samples. In this paper, we present a structure–attention network to preserve the target structure from the structure distortion caused by the boundary effect. More specifically, we adopt a variational auto-encoder as a structure–attention network to make various and representative target structures. We also proposed two denoising criteria using a novel reconstruction loss for variational auto-encoding framework to capture more robust structures even under the boundary condition. Through the proposed structure–attention framework, discriminative correlation filters can learn robust structure information of targets during online training with an enhanced discriminating performance and adaptability. Experimental results on major visual tracking benchmark datasets show that the proposed method produces a better or comparable performance compared with the state-of-the-art tracking methods with a real-time processing speed of more than 80 frames per second.

Real-time visual tracking using complementary kernel support correlation filters

2019 ◽  
Vol 14 (2) ◽  
pp. 417-429 ◽  
Author(s):  
Zhenyang Su ◽  
Jing Li ◽  
Jun Chang ◽  
Bo Du ◽  
Yafu Xiao
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Correlation Filters

scholarly journals Parallel Correlation Filters for Real-Time Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2362 ◽  
Author(s):  
Yijin Yang ◽  
Yihong Zhang ◽  
Demin Li ◽  
Zhijie Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Research Field ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Visual Object ◽  
Correlation Filters ◽  
Illumination Variation ◽  
Visual Object Tracking ◽  
Appearance Changes

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.

scholarly journals Robust Scale Adaptive and Real-Time Visual Tracking with Correlation Filters

2016 ◽  
Vol E99.D (7) ◽  
pp. 1895-1902
Author(s):  
Jiatian PI ◽  
Keli HU ◽  
Yuzhang GU ◽  
Lei QU ◽  
Fengrong LI ◽  
...  
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Correlation Filters

scholarly journals Robust Visual Tracking with Reliable Object Information and Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 889
Author(s):  
Hang Chen ◽  
Weiguo Zhang ◽  
Danghui Yan
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Online Training ◽  
Linear Interpolation ◽  
Correlation Filter ◽  
Target Information ◽  
Filter Methods ◽  
Tracking Process ◽  
Background Clutter

Object information significantly affects the performance of visual tracking. However, it is difficult to obtain accurate target foreground information because of the existence of challenging scenarios, such as occlusion, background clutter, drastic change of appearance, and so forth. Traditional correlation filter methods roughly use linear interpolation to update the model, which may lead to the introduction of noise and the loss of reliable target information, resulting in the degradation of tracking performance. In this paper, we propose a novel robust visual tracking framework with reliable object information and Kalman filter (KF). Firstly, we analyze the reliability of the tracking process, calculate the confidence of the target information at the current estimated location, and determine whether it is necessary to carry out the online training and update step. Secondly, we also model the target motion between frames with a KF module, and use it to supplement the correlation filter estimation. Finally, in order to keep the most reliable target information of the first frame in the whole tracking process, we propose a new online training method, which can improve the robustness of the tracker. Extensive experiments on several benchmarks demonstrate the effectiveness and robustness of our proposed method, and our method achieves a comparable or better performance compared with several other state-of-the-art trackers.

scholarly journals Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

2021 ◽  
Author(s):  
Libin Xu ◽  
Pyoungwon Kim ◽  
Mengjie Wang ◽  
Jinfeng Pan ◽  
Xiaomin Yang ◽  
...  
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Boundary Effect ◽  
Correlation Filter ◽  
Star Model ◽  
Unified Framework ◽  
Alternating Direction ◽  
Background Clutter ◽  
Discriminative Correlation Filter ◽  
Spatio Temporal

AbstractThe discriminative correlation filter (DCF)-based tracking methods have achieved remarkable performance in visual tracking. However, the existing DCF paradigm still suffers from dilemmas such as boundary effect, filter degradation, and aberrance. To address these problems, we propose a spatio-temporal joint aberrance suppressed regularization (STAR) correlation filter tracker under a unified framework of response map. Specifically, a dynamic spatio-temporal regularizer is introduced into the DCF to alleviate the boundary effect and filter degradation, simultaneously. Meanwhile, an aberrance suppressed regularizer is exploited to reduce the interference of background clutter. The proposed STAR model is effectively optimized using the alternating direction method of multipliers (ADMM). Finally, comprehensive experiments on TC128, OTB2013, OTB2015 and UAV123 benchmarks demonstrate that the STAR tracker achieves compelling performance compared with the state-of-the-art (SOTA) trackers.

scholarly journals Learning Robust Gaussian Process Regression for Visual Tracking

2018 ◽  
Author(s):  
Linyu Zheng ◽  
Ming Tang ◽  
Jinqiao Wang
Keyword(s):  
Gaussian Process ◽  
Visual Tracking ◽  
Boundary Effect ◽  
Gaussian Process Regression ◽  
Correlation Filter ◽  
Kernel Trick ◽  
Recent Developments ◽  
Fast Optimization ◽  
Public Datasets ◽  
The Cost

Recent developments of Correlation Filter based trackers (CF trackers) have attracted much attention because of their top performance. However, the boundary effect imposed by the basic periodic assumption in their fast optimization seriously degrades the performance of CF trackers. Although there existed many recent works to relax the boundary effect in CF trackers, the cost was that they can not utilize the kernel trick to improve the accuracy further. In this paper, we propose a novel Gaussian Process Regression based tracker (GPRT) which is a conceptually natural tracking approach. Compared to all the existing CF trackers, the boundary effect is eliminated thoroughly and the kernel trick can be employed in our GPRT. In addition, we present two efficient and effective update methods for our GPRT. Experiments are performed on two public datasets: OTB-2013 and OTB-2015. Without bells and whistles, on these two datasets, our GPRT obtains 84.1% and 79.2% in mean overlap precision, respectively, outperforming all the existing trackers with hand-crafted features.

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