Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Xu, Libin; Kim, Pyoungwon; Wang, Mengjie; Pan, Jinfeng; Yang, Xiaobo; Gao, Mingliang

doi:10.1007/s40747-021-00544-1

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…The aberrance repressed regularization with a fixed control coefficient in [31] is employed to alleviate abnormal responses. Furthermore, [32]- [35] also introduce the aberrance repressed regularization into the DCF-based model and use constant regularization parameters to participate in model training.…”

Section: B Tracking With Aberrance Repressed Regularizationmentioning

confidence: 99%

UAV Tracking Based on Correlation Filters With Dynamic Aberrance-Repressed Temporal Regularizations

Zhang,

Li,

Yang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As a significant research direction in remote sensing fields, unmanned aerial vehicles (UAVs) tracking has achieved rapid development in recent years. However, due to limited power and computation resources on aerial platforms, the tracking methods deployed on UAVs usually require high computational efficiency and performance. In addition, various challenges (i.e., similar object, background clutter and occlusion) have inevitably occurred during the UAV tracking phase. Therefore, considering the above issues comprehensively, this paper proposes a dynamic aberrance-repressed temporal regularized correlation filter to achieve stable tracking in UAV remote sensing videos. First, we have introduced the aberrance-repressed temporal regularizations into the discriminative correlation filter (DCF) framework. Second, a novel objective loss function is constructed to adjust the strength of each regularization for training the filter. Then, a new judgment mechanism based on the response variation is exploited to reflect the response fluctuation and applied to tune parameters of both regularizations. Finally, comprehensive experiments are done on three different UAV benchmarks, i.e., UAV123@10fps, UAVDT and VisDrone2018, to verify the performance of our tracker and have demonstrated that our tracker achieves superior performance against other total 25 state-of-the-art trackers while reaching ∼35 FPS on a single CPU.

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Section: B Tracking With Aberrance Repressed Regularizationmentioning

confidence: 99%

UAV Tracking Based on Correlation Filters With Dynamic Aberrance-Repressed Temporal Regularizations

Zhang,

Li,

Yang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…LADCF [35] incorporates temporal consistency constraints into the model for enhanced tracker robustness and reduced model degradation. In recent work, many trackers incorporated spatial-temporal regularization [36][37][38], while the tracking performance has been significantly improved. Yu et al [36] proposed a second-order spatial-temporal correlation filter (SSCF), which incorporates both the first-order and second-order data-fitting terms into the DCF framework.…”

Section: Related Workmentioning

confidence: 99%

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Sun

et al. 2022

Symmetry

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With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.

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“…However, such passive learning strategies may not be effective, as they do not prevent the occurrence of response aberrations. To achieve background suppression, some methods [17][18][19][20] use the known previous frame's response as a template to limit the variation rate of the current frame's response, thus effectively limiting drastic response changes. Some trackers [6,17,21,22] suppress the background in a very straightforward way, i.e., using a spatial constraint matrix to mask or ignore the background region.…”

Section: Introductionmentioning

confidence: 99%

Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking

Sun

et al. 2023

Sensors

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The discriminative correlation filter (DCF)-based tracking method has shown good accuracy and efficiency in visual tracking. However, the periodic assumption of sample space causes unwanted boundary effects, restricting the tracker’s ability to distinguish between the target and background. Additionally, in the real tracking environment, interference factors such as occlusion, background clutter, and illumination changes cause response aberration and, thus, tracking failure. To address these issues, this work proposed a novel tracking method named the background-suppressed dual-regression correlation filter (BSDCF) for visual tracking. First, we utilize the background-suppressed function to crop out the target features from the global features. In the training step, while introducing the spatial regularity constraint and background response suppression regularization, we construct a dual regression structure to train the target and global filters separately. The aim is to exploit the difference between the output response maps for mutual constraint to highlight the target and suppress the background interference. Furthermore, in the detection step, the global response can be enhanced by a weighted fusion of the target response to further improve the tracking performance in complex scenes. Finally, extensive experiments are conducted on three public benchmarks (including OTB100, TC128, and UAVDT), and the experimental results indicate that the proposed BSDCF tracker achieves tracking performance comparable to many state-of-the-art (SOTA) trackers in a variety of complex situations.

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Spatio-temporal joint aberrance suppressed correlation filter for visual tracking

Cited by 10 publications

References 46 publications

UAV Tracking Based on Correlation Filters With Dynamic Aberrance-Repressed Temporal Regularizations

UAV Tracking Based on Correlation Filters With Dynamic Aberrance-Repressed Temporal Regularizations

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Learning Background-Suppressed Dual-Regression Correlation Filters for Visual Tracking

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