Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization

Ye, Junjie; Fu, Changhong; Lin, Fuling; Ding, Fangqiang; An, Shan; Lu, Geng

doi:10.1109/tie.2021.3088366

Cited by 59 publications

(27 citation statements)

References 63 publications

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“…35 They usually formulate new regularization terms to guide the filter to learn in their desired direction. [36][37][38][39][40][41] Huang et al 36 construct an aberrance repressed regularization term to prevent sudden changes in the response map to deal with severe background interference. Li et al 37 introduce a new spatio-temporal regularization term and automatically adjust hyperparameters according to local and global response maps.…”

Section: Handcrafted Features Based Correlation Filter Trackingmentioning

confidence: 99%

“…Recently, handcrafted features‐based DCF trackers have shown considerable advantages in UAV tracking 35 . They usually formulate new regularization terms to guide the filter to learn in their desired direction 36–41 . Huang et al 36 construct an aberrance repressed regularization term to prevent sudden changes in the response map to deal with severe background interference.…”

Section: Related Workmentioning

confidence: 99%

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Robust visual tracking via adaptive feature channel selection

Zhang

Hou

et al. 2022

Int J of Intelligent Sys

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Discriminative correlation filters (DCFs) have shown promising tracking performance in recent years thanks to the powerful representation ability of deep features.However, a large number of target-irrelevant channels in deep features limits the tracking performance and increases the computational cost. To eliminate the negative impact of noisy channels and improve the utilization efficiency of deep features in DCF-based trackers, we present an adaptive feature channel selection method for robust visual tracking. Our method adaptively chooses the most discriminative channels to learn a more robust target appearance model, which is achieved by evaluating the energy relationship between background and foreground in each feature channel. Moreover, according to the feedback of channel selection, an adaptive model update strategy is proposed to alleviate the model degradation problem caused by incorrect model updating. Extensive experimental results obtained on five popular tracking benchmarks demonstrate the effectiveness of the proposed algorithm and its superiority over the state-of-the-art trackers.

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Section: Handcrafted Features Based Correlation Filter Trackingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust visual tracking via adaptive feature channel selection

Zhang

Hou

et al. 2022

Int J of Intelligent Sys

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show abstract

“…Considering the high processing accuracy and computational efficiency of DCF, most of the existing UAV trackers are implemented based on the DCF algorithm [25][26][27][28], so as to meet the real-time requirements of UAV tracking. ARCF [25] designs a cropping matrix and a regularization term to enlarge search region and aberrance repression, effectively improves the robustness and accuracy of the tracker.…”

Section: Trackers For Uav Videosmentioning

confidence: 99%

“…AutoTrack [26] introduces the spatially local response map variation as spatial regularization, proposed a novel approach to online automatically and adaptively learn spatial regularization term. Ye et al [27] propose a tracking algorithm based on a multi-regularized correlation filter. The tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability.…”

Section: Trackers For Uav Videosmentioning

confidence: 99%

MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

Liu

Zhang

et al. 2021

Remote Sensing

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Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks.

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“…He et al[57] introduced a unified tri-attention framework to leverage multi-level visual information, including contextual, spatiotemporal and dimension attention to improve UAV tracking robustness and efficiency. Ye et al[167] proposed a novel tracking framework based on a multi-regularized correlation filter, which leads to favorable adaption to object appearance variations and enhancement of discriminability. Zhang et al[178] exploited a two-stage scheme that combines a detection-based network (IoU-Net) with DCF-based tracker for object tracking in aerial videos.…”

mentioning

confidence: 99%

The State of Aerial Surveillance: A Survey

Nguyen¹,

Fookes²,

Sridharan³

et al. 2022

Preprint

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The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.

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Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization

Cited by 59 publications

References 63 publications

Robust visual tracking via adaptive feature channel selection

Robust visual tracking via adaptive feature channel selection

MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

The State of Aerial Surveillance: A Survey

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