Path planning with Incremental Roadmap Update for Visibility-based Target Tracking

Laguna, Guillermo J.; Bhattacharya, Sourabh

doi:10.1109/iros40897.2019.8967602

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Visual object tracking is a fundamental and challenging task, whose purpose is to track the indicated object frame by frame. By virtue of the powerful flexibility of unmanned aerial vehicles (UAVs), UAV tracking has drawn considerable attention such as aerial cinematography [1], path planning [2], and maneuvering targets tracking [3]. Despite numerous efforts, designing an efficient, accurate, and robust tracker for UAV remains an extremely challenging task due to the limited computational resources onboard the embedded platform.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Cao¹,

Fu²,

Ye³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Aerial photography instruments have light sensitivities that differ among targets and are limited by their own flight height. In aerial photography images, there are often targets that are visible to the naked eye but have a small pixel size and image objects that blur or resemble the actual background color texture [ 7 , 8 ]. This study conducts a classification based on the characteristics of aerial photography targets according to the following six types: Dim small targets: Targets for which the imaging size is relatively small due to the shooting angle and shooting distance-namely, targets for which the imaging size is less than 0.12% of the total number of pixels [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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“…Nowadays, due to the unmatched mobility and portability, unmanned aerial vehicle (UAV) has aroused widespread attention for various applications, such as path planning [1], autonomous landing [2], obstacle avoidance [3], and aerial cinematography [4]. As the basis of the above applications, developing a real-time, robust and accurate tracking method is imperative.…”

Section: Introductionmentioning

confidence: 99%

Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking

Yang

et al. 2020

Preprint

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Correlation filter (CF)-based methods have demonstrated exceptional performance in visual object tracking for unmanned aerial vehicle (UAV) applications, but suffer from the undesirable boundary effect. To solve this issue, spatially regularized correlation filters (SRDCF) proposes the spatial regularization to penalize filter coefficients, thereby significantly improving the tracking performance. However, the temporal information hidden in the response maps is not considered in SRDCF, which limits the discriminative power and the robustness for accurate tracking. This work proposes a novel approach with dynamic consistency pursued correlation filters, i.e., the CPCF tracker. Specifically, through a correlation operation between adjacent response maps, a practical consistency map is generated to represent the consistency level across frames. By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced. Besides, a dynamic constraint strategy is proposed to further improve the adaptability of the proposed tracker in complex situations. Comprehensive experiments are conducted on three challenging UAV benchmarks, i.e., UAV123@10FPS, UAVDT, and DTB70. Based on the experimental results, the proposed tracker favorably surpasses the other 25 state-of-the-art trackers with real-time running speed (∼43FPS) on a single CPU.

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Path planning with Incremental Roadmap Update for Visibility-based Target Tracking

Cited by 12 publications

References 15 publications

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

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

Aerial Video Trackers Review

Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking

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