Learning target-aware background-suppressed correlation filters with dual regression for real-time UAV tracking

zhang, Fei; Ma, Shiping; Qiu, Zhuling; Qi, Tao

doi:10.1016/j.sigpro.2021.108352

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Zhang et al. [ 14 ] proposed a tracking method with dual regression for target awareness and background suppression (TABSCF), which adopts the saliency detection method to extract the target features. Moreover, TABSCF uses target features and extra global features to regress the target filter and the global filter, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…In others words, the actual distribution contains multi-peaks that significantly affect the target location. To suppress the multi-peak response interferences, some methods [ 13 , 14 , 15 ] adopt the response regularization to smooth response variations and repress the abnormal response peaks appearing in the current frame. Some methods [ 16 , 17 ] introduce the context information into the DCF framework for training the filter, which effectively reduces the response fluctuations of distractors.…”

Section: Introductionmentioning

confidence: 99%

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Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

Zhang

Liu

et al. 2023

Sensors

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With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the l2-norm constraint for consistency, this module not only can avoid sudden changes of the target response caused by background interferences but also enables the learned filter to preserve the discriminative ability of the previous filter. Second, a novel background-suppressed module is proposed, which makes the learned filter to be more aware of background information by using an attention mask matrix. With the introduction of this module into the DCF framework, the proposed method can further suppress the response interferences of distractors in the background. Finally, extensive comparative experiments have been conducted on three challenging UAV benchmarks, including UAV123@10fps, DTB70 and UAVDT. Experimental results have proved that our tracker has better tracking performance compared with 22 other state-of-the-art trackers. Moreover, our proposed tracker can run at ∼36 FPS on a single CPU for real-time UAV tracking.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

Zhang

Liu

et al. 2023

Sensors

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“…Huang et al [17] proposed aberrance repressed correlation filters (ARCF), which is to suppress response distortion due to background interference by minimizing the Euclidean distance between global response maps of consecutive frames. Zhang et al [49] proposed the target-aware background-suppressed correlation filters (TABSCF), which generate a binary mask matrix by employing saliency detection algorithm to distinguish target region from the background. Furthermore, it employs the dual regression model to train the filter so as to suppress background response fluctuations.…”

Section: Dcf Tacking With Background Suppressionmentioning

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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“…With the development of technology, more and more UAVs can be seen in both the military and civilian fields. [1][2][3][4][5]. In these studies, the control of multi-UAV formation tracking is a hot topic [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation

Jia

Chen

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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This paper investigates a circular formation control strategy for multiple unmanned aerial vehicles (multi-UAV) formation for ground moving target tracking. Each UAV obtains the observation value of target information through sensors and exchanges data with its neighbors. Since the formation cannot get the parameter of the target directly, we proposed a distributed error observer. A new guidance law based on the error value obtained has been designed, and it was proved through the interconnection system that it was consistent with Lyapunov stability. Finally, a simulation example is used to illustrate the effectiveness of the proposed guidance law.

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Learning target-aware background-suppressed correlation filters with dual regression for real-time UAV tracking

Cited by 8 publications

References 11 publications

Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

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

A Distributed Control for Ground Target Tracking of Nonlinear Unmanned Aerial Vehicles Formation

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