Robust Visual Tracking with Channel Weighted Color Ratio Feature

Jiang, Shan; Li, Shuxiao; Zhu, Chengfei; Lan, Xiaosong

doi:10.1109/icivc47709.2019.8980966

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Finally, the overall employed feature representation

{\vartheta }_{{\Omega}}

has 23 channels. Moreover, following the setting in the baseline tracker 13 and the typical parameters in the existing literatures, 30,33 the other parameters for our framework are listed in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Correlation filters tissue tracking with application to robotic minimally invasive surgery

Sun

2022

Robotics Computer Surgery

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Purpose Vision‐based tissue tracking is a significant component for building efficient autonomous surgical robot system. While the methodology involves various challenges caused by occlusion, deformation and appearance changes. Methods We propose a novel correlation filter tissue tracking framework for minimally invasive surgery. Our model contains the innovative design of synthetic features, a bi‐branch is exploited to enhance the response map. An incrementally learnt detector with the novel updating and trigger schemes is embedded to model the re‐detection module for capturing the lost target. Results Promising validation has been conducted on the publicly available tracking benchmark datasets, a surgical tissue tracking dataset based on publicly available Cholec80 dataset has also been developed to focus on the application in intra‐operative scenes. Conclusions Our proposed framework meets the outstanding performance and surpasses the existing methods. The work demonstrates the feasibility to perform tissue tracking by taking advantage of the correlation filter.

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“…Finally, the overall employed feature representation

{\vartheta }_{{\Omega}}

Section: Methodsmentioning

confidence: 99%

Correlation filters tissue tracking with application to robotic minimally invasive surgery

Sun

2022

Robotics Computer Surgery

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show abstract

“…In this section, we first give a brief introduction of our previous CRCF tracker [ 7 ]. Then, we present the overall process of our CRCF_ATU tracker with an adaptive training sample update scheme and describe some details.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In order to make better use of target and background color information to improve the performance of tracker, we borrow the weight map idea of mean shift algorithm to propose CR features [ 7 ], and integrate them into the correlation tracking framework to obtain the CRCF tracker, which improves the tracking speed and performance.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Aiming at the problem of model drift in correlation tracking model updating, CRCF_ATU is proposed on the basis of the CRCF [ 7 ] and the adaptive samples set the updating mechanism based on the difference hashing algorithm (DHA) [ 11 ]. Unlike our previous work which trains and updates correlation filter in each frame, we adaptively maintain a training samples set and train the correlation filter on it.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…However, traditional DCF trackers such as [ 2 ] are affected by boundary effects, while other improved DCF trackers [ 3 , 4 ] suffer from a large amount of calculations or unsatisfied model updating strategy, which cannot meet the requirements of target tracking when the computing resources are strictly restricted. In order to achieve balance between performance and efficiency of the tracker, we borrow the idea of mean shift tracking algorithms [ 5 , 6 ] to build a new color ratio feature (CR), and propose a DCF-based tracker embedded with CR features, namely CRCF tracker [ 7 ], which can achieve robust performance with real-time speed. Although our previous works have made great progress, nevertheless, a simple moving average update scheme of CRCF tracker cannot deal with the problem of occlusion and large appearance variation, and usually leads to model contamination and tracking drift.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient and Practical Correlation Filter Tracking

Zhu

Jiang

et al. 2021

Sensors

Self Cite

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Visual tracking is a basic task in many applications. However, the heavy computation and low speed of many recent trackers limit their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter-based trackers restricts their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter-based trackers and propose an efficient and adaptive training sample update scheme. The training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with a difference hashing algorithm or discarded according to tracking result reliability. In addition, we expand our new tracker to long-term tracking. On the basis of the proposed model updating mechanism, we propose a new tracking state discrimination mechanism to accurately judge tracking failure, and resume tracking after the target is recovered. Experiments on OTB-2015, Temple Color 128 and UAV123 (including UAV20L) demonstrate that our tracker performs favorably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2 GHz).

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Efficient correlation filter tracking with adaptive training sample update scheme

Jiang

Zhu

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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Robust Visual Tracking with Channel Weighted Color Ratio Feature

Cited by 3 publications

References 16 publications

Correlation filters tissue tracking with application to robotic minimally invasive surgery

Correlation filters tissue tracking with application to robotic minimally invasive surgery

Efficient and Practical Correlation Filter Tracking

Efficient correlation filter tracking with adaptive training sample update scheme

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