Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking

Fu, Changhong; Ye, Junjie; Xu, Juntao; He, Yujie; Lin, Fuling

doi:10.1109/tgrs.2020.3030265

Cited by 39 publications

(18 citation statements)

References 56 publications

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“…Object tracking approaches can be broadly divided into methods based on correlation filters (CF) [21], [22], [23] and methods based on convolutional neural network (CNN) [11], [24], [25]. Due to their efficiency [5], [22], CF-based trackers were considered as the promising choice for UAV tracking. In the beginning, MOSSE [26] has drawn increasing attention to the CF-based tracker.…”

Section: Related Work a Cf-based Uav Trackingmentioning

confidence: 99%

Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

Fu¹,

Li²,

Yuan³

et al. 2022

Preprint

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Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related applications, which leads to a highly demanding requirement on the robustness of UAV trackers. However, adding imperceptible perturbations can easily fool the tracker and cause tracking failures. This risk is often overlooked and rarely researched at present. Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i.e., Ad 2 Attack, against UAV object tracking. Specifically, adversarial examples are generated online during the resampling of the search patch image, which leads trackers to lose the target in the following frames. Ad 2 Attack is composed of a direct downsampling module and a super-resolution upsampling module with adaptive stages. A novel optimization function is proposed for balancing the imperceptibility and efficiency of the attack. Comprehensive experiments on several well-known benchmarks and real-world conditions show the effectiveness of our attack method, which dramatically reduces the performance of the most advanced Siamese trackers.

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Section: Related Work a Cf-based Uav Trackingmentioning

confidence: 99%

Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

Fu¹,

Li²,

Yuan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Depending on the high efficiency and expansibility, CF-based trackers can be deployed directly on UAVs. There is no doubt that the CFbased approach promoted the development of UAV tracking with satisfying speed [5], [15], [16]. However, the online tracking strategy hinders inevitably the structure of trackers, hardly learning and exploiting deeper features.…”

Section: Related Workmentioning

confidence: 99%

SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking

Cao¹,

Fu²,

Ye³

et al. 2021

Preprint

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Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its stateof-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, e.g., severe occlusion, and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency. To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking. By virtue of the attention mechanism, the attentional aggregation network (AAN) is conducted with self-AAN and cross-AAN, raising the expression ability of features eventually. The former AAN aggregates and models the self-semantic interdependencies of the single feature map via spatial and channel dimensions. The latter aims to aggregate the cross-interdependencies of different semantic features including the location information of anchors. In addition, the dual features version of the anchor proposal network is proposed to raise the robustness of proposing anchors, increasing the perception ability to objects with various scales. Experiments on two well-known authoritative benchmarks are conducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA trackers. Besides, realworld tests onboard a typical embedded platform demonstrate that SiamAPN++ achieves promising tracking results with realtime speed.

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“…Generally, as a result of its high-speed, DCF-based trackers have been widely adopted on UAV tracking. The DCFbased approach took the object tracking algorithms to a new level, greatly promoted the robustness and accuracy with satisfying speed [5], [15]- [17]. In recent years, siamesebased trackers have become attractive due to its state-ofthe-art performance which is also another choice for UAV tracking.…”

Section: Related Workmentioning

confidence: 99%

Siamese Anchor Proposal Network for High-Speed Aerial Tracking

Cao

et al. 2020

Preprint

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In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency, thereby impeding their real-world deployment on mobile platforms like the unmanned aerial vehicle (UAV). In this work, a novel two-stage siamese network-based method is proposed for aerial tracking, i.e., stage-1 for high-quality anchor proposal generation, stage-2 for refining the anchor proposal. Different from anchor-based methods with numerous pre-defined fixed-sized anchors, our no-prior method can 1) make tracker robust and general to different objects with various sizes, especially to small, occluded, and fast-moving objects, under complex scenarios in light of the adaptive anchor generation, 2) make calculation feasible due to the substantial decrease of anchor numbers. In addition, compared to anchorfree methods, our framework has better performance owing to refinement at stage-2. Comprehensive experiments on three benchmarks have proven the state-of-the-art performance of our approach, with a speed of ∼200 frames/s.

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Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking

Cited by 39 publications

References 56 publications

Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking

Siamese Anchor Proposal Network for High-Speed Aerial Tracking

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