Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping

Peng, Fang; Xu, Qinyi; Li, Yifei; Zheng, Maoxi; Su, Hang

doi:10.1109/tim.2022.3195258

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…In comparison, we validate our relation discrimination (Att-DCF) against 11 recent top trackers: LADCF [14], ASLA [37], IVT [38], C-cot [34], SRDCF [20], BACF [33], MEEM [39], STAPLE [23], CSRDCF [40], DSST [18], and KCF [17]. A one-pass evaluation using distance accuracy and overlap performance testing shows that the tracker outperforms competing trackers in both tests, as shown in Table Table 3 and Figure Fig.…”

Section: Experimental Results Of Uav-123 Datasetmentioning

confidence: 77%

“…8 shows not only this comparison but also the results of the various monitoring strategies compared. It shows the accuracy of LADCF [14], ECO [32], BACF [33], C-cot [34], SRDCF [20], STAPLE [23], DSST [18], SAMF [35], and KCF [17] as well as the proposed Att-DCF approach on various challenging sequences, including those with rapid changes in the target's appearance change, object-deformations, out-of-view, partial-occlusions, and in-plane and out-of-plane rotations.…”

Section: Experimental Results Of Uav-123 Datasetmentioning

confidence: 92%

“…Danwelljan et al get. introduced the Kernel Correlation Filter (KCF), which improves accuracy by using multiple-level features [17]. However, the object rotation, get out of view and moving fast are still challenging.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking

Elsaid,

Fouad,

Ghoniemy

2024

Preprint

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Correlation Filter-based Trackers have shown impressive results in the object tracking research, outperforming classical trackers in several benchmarks. However, accurately tracking objects with deformation, fast motion, or occlusion remains a main challenge in the process of tracking. The cyclic suggestion of training samples used in correlation filter tracking usually lead to undesirable boundary effects, that significantly reduce the tracking efficiency. To address this issues, a hybrid attention-based correlation filter method (Att-DCF) is proposed for robust object tracking. By developing a discrimination filter and reducing the limitations of irrelevant features, the proposed approach provides more accurate and consistent estimated target locations. The proposed Att-DCF tracker is evaluated using the recent and standard datasets, OTB-100, Temple-Colour128, and UAV123. Compared to the baseline DCF tracker, the proposed tracker achieves an improvement of 2.71$\%$ and 1.38$\%$ in terms of area under the curve and precision measures using the OTB-100 dataset, 3.99$\%$ and 3.43$\%$ using the Temple-Colour128, and 4.72$\%$ and 1.76$\%$ using the UAV123, respectively.

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Section: Experimental Results Of Uav-123 Datasetmentioning

confidence: 77%

Section: Experimental Results Of Uav-123 Datasetmentioning

confidence: 92%

See 1 more Smart Citation

Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking

Elsaid,

Fouad,

Ghoniemy

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Visual tracking is a vital application in bionic robot technology [ 1 – 3 ], which endows robots with the ability to track a specified target by analyzing image data and to autonomously control their motion [ 4 ]. However, visual tracking poses unique challenges for robot vision, as the camera is usually fixed on the robot platform and the target may undergo nonrigid deformation because of the change of perspective caused by the relative motion between the robot and the target [ 5 , 6 ]. It results in dynamic blur and nonrigid deformation, which degrades the performance of the tracker.…”

Section: Introductionmentioning

confidence: 99%

Practical Tracking Method based on Best Buddies Similarity

Chen

Liu

et al. 2023

Cyborg Bionic Syst

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Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.

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“…With the continuous updating and development of technology, specific moving target tracking can be applied in many scenarios, such as industrial automation identification 1,2 , video surveillance 3,4 , and robot vision 5 . In the current related research, single-target tracking is one of the research hotspots for specific moving target tracking 6,7 , which aims to accurately and continuously localize the specific single object.…”

Section: Introductionmentioning

confidence: 99%

Tracking moving targets of interest-based deep learning

Shangguan,

Gong,

Chen

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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Tracking specific target is an important research area in machine vision. When tracking specific target encounters the same local features with light and dark variations, convolutional neural networks based on deep learning require large data sets to extract target feature information. To address these problems, this paper proposes a target of interest tracking scheme based on specific object imaging. The method allows for fast capture and processing of important information about the target of interest. In addition, small data sets are used to track the target and extract the target of interest efficiently. The target of interest can also be accurately tracked when the background and light changes. The results show that our method reduces the processing of redundant information, reduces the storage overhead, and tracks the target accurately.

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Improved Kernel Correlation Filter Based Moving Target Tracking for Robot Grasping

Cited by 13 publications

References 33 publications

Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking

Leveraging Attention for Improved Discriminative Correlation Filters in Single Object Tracking

Practical Tracking Method based on Best Buddies Similarity

Tracking moving targets of interest-based deep learning

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