An Experimental Survey on Correlation Filter-based Tracking

Chen, Zhe; Hong, Zhibin; Tao, Dacheng

doi:10.48550/arxiv.1509.05520

Cited by 8 publications

(11 citation statements)

References 41 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, we are also interested in investigating computational efficient long term tracking strategy or spatial regularization to further augment tracking accuracy. [71]).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Zhang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

View full text Add to dashboard Cite

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. Experimental results demonstrate the superiority of our proposed approach when compared with other state-of-the-art trackers. MATLAB code is available from our project homepage http://bmal.hust.edu.cn/ project/KMF2JMTtracking.html.

show abstract

“…At the same time, we are also interested in investigating computational efficient long term tracking strategy or spatial regularization to further augment tracking accuracy. [71]).…”

Section: Discussionmentioning

confidence: 99%

“…We also attempt to illustrate the future research directions of CFTs through a comprehensive evaluation. Table III summarized the basic information of the selected CFTs as well as their corresponding FPS values (some descriptions are adapted from [71]).…”

Section: F Comparison Among Correlation Filter-based Trackers (Cfts)mentioning

confidence: 99%

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Zhang

et al. 2019

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a correlation filter-based tracking algorithm, MOSSE directly uses the appearance (pixels) feature of images to produce correlation peaks for each interested target in the scene while yielding low responses to the background. To obtain better performance, multi-channel HOG [56] feature is applied in KCF [57]. Furthermore, the SiamFc employs CNN to extract features of interested targets, which is extremely effective compared with a HOG feature and appearance (pixels) feature.…”

Section: Shadow Trackingmentioning

confidence: 99%

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

et al. 2020

View full text Add to dashboard Cite

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

show abstract

“…Recently the correlation filter based discriminative model has aroused wide attention in the field of visual tracking. After Minimum Output Sum of Squared Error (MOSSE) [4] filter was proposed, numerous correlation filter methods start flowing in the field of computer vision [7]. Henriques et al [16] used the kernel trick, and Danelljan et al [12] represented the inputs with color attributes.…”

Section: Related Workmentioning

confidence: 99%

Spectral Filter Tracking

Cui

Cai

Zheng

et al. 2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Visual object tracking is a challenging computer vision task with numerous real-world applications. Here we propose a simple but efficient Spectral Filter Tracking (SFT) method. To characterize rotational and translation invariance of tracking targets, the candidate image region is models as a pixelwise grid graph. Instead of the conventional graph matching, we convert the tracking into a plain least square regression problem to estimate the best center coordinate of the target. But different from the holistic regression of correlation filter based methods, SFT can operate on localized surrounding regions of each pixel (i.e., vertex) by using spectral graph filters, which thus is more robust to resist local variations and cluttered background. To bypass the eigenvalue decomposition problem of the graph Laplacian matrix L, we parameterize spectral graph filters as the polynomial of L by spectral graph theory, in which L k exactly encodes a k-hop local neighborhood of each vertex. Finally, the filter parameters (i.e., polynomial coefficients) as well as feature projecting functions are jointly integrated into the regression model.SFT can simply boil down to only a few line codes, but surprisingly it beats the correlation filter based model with the same feature input, and achieves the current best performance on the dataset [36] under the same feature extraction strategy (i.e., the existing VGG-Net model [32]). The code will be fully released in our website soon.Zhen Cui is with the

show abstract

An Experimental Survey on Correlation Filter-based Tracking

Cited by 8 publications

References 41 publications

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

Spectral Filter Tracking

Contact Info

Product

Resources

About