STN-Track: Multiobject Tracking of Unmanned Aerial Vehicles by Swin Transformer Neck and New Data Association Method

Xu, Xiangkai; Feng, Zhejun; Cao, Chuanbao; Yu, Chaoran; Li, Mengyuan; Wu, Zengyan; Ye, Shubing; Shang, Yajie

doi:10.1109/jstars.2022.3213438

Cited by 20 publications

(8 citation statements)

References 35 publications

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“…[44] addresses low confidence detections during occlusion and separates high and low score frames to identify real objects. [45] applies [44] to UAV tracking, but different treatments are needed for high and low scoring frames in different scenarios.…”

Section: Tracking Resultsmentioning

confidence: 99%

Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Shi,

Zhang,

Pan

et al. 2023

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

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Multi-object tracking in unmanned aerial vehicle (UAV) scenes is a crucial task with numerous applications across various domains. The goal of this task is to track multiple objects such as people and vehicles over time as they move through the scene, often captured by cameras mounted on UAV. However, tracking objects in UAV scenes can be challenging due to several factors, including the scale variance of objects as they move through the scene and the frequent occlusions caused by complex scenes. To address these issues, we propose a Global-Local and Occlusion Awareness (GLOA) tracking network for unmanned aerial vehicles. It comprises two main components: global-local awareness detector (GLA-D) and the occlusion awareness data association (OADA). The GLA-D uses our specially designed global-local awareness block to extract scale variance feature information from the input frames. It then outputs more discriminative identity information by adding identity embedding branches to the prediction head. The GLA-D is designed to better handle scale variance issues and improve object tracking accuracy. The OADA method used different metrics for high and low scoring detection frames was to alleviate occlusion problems in tracking scenarios. By combining these two components, the GLOA provides a more robust and effective solution for multi-object tracking in UAV scenes. Experiments on two public datasets may indicate the effectiveness of the proposed method.

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Section: Tracking Resultsmentioning

confidence: 99%

Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Shi,

Zhang,

Pan

et al. 2023

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

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“…For example, 𝑥 𝑡 𝑘 ̂ is the Fourier transform of 𝑥 𝑡 𝑘 . It is too difficult to solve (7) directly, so the above equation can be solved after further simplification.…”

Section: 𝐸(𝐻mentioning

confidence: 99%

“…Nevertheless, a large number of algorithms have been proposed for achieving accurate tracking. These algorithms can be broadly categorized into deep learning [7]- [12] and correlation filters [13]- [19]. Deep learning extracts high-dimensional semantic information about the target mainly with the help of convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

“…Unlike handcrafted features such as HOG and color names (CN), the deep features extracted by CNN are more resistant to interference, and they are less susceptible to environmental influences. SwinTransformer neck and new data association method (STN-Track) [7] uses the swinTransformer as a backbone network for feature extraction, which significantly enhances the global interaction capability of the model. Similar to STN-Track, hierarchical feature pooling Transformer (HFPT) [8] also uses Transformer in the tracking framework to enhance the representational information of small targets.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Spatial Regularization Correlation Filters for UAV Tracking

Cao,

Dong,

Zhang

et al. 2024

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

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As a tool for near-earth remote sensing, unmanned aerial vehicle (UAV) can be used to acquire images and data of the earth's surface. This provides a powerful support for Earth observation and resource management. Object tracking in UAV videos has been a topic of much interest in recent years. A large number of algorithms have been proposed. Among these algorithms, deep learning has achieved a high accuracy rate. However, it is difficult to carry hardware devices for UAV, which makes it difficult to be practically applied. The correlation filter does not require a graphic processing unit (GPU) to accelerate the computation, but it uses only manual features, which makes it difficult to achieve satisfactory performance. In order to solve the above problems, we proposed adaptive spatial regularization correlation filters, called DTSRT. Specifically, we first introduce deep features in the correlation filter instead of the original manual features, which can greatly improve the discriminative ability of the model. At the same time, in order to prevent affecting the real-time performance of the algorithm, we use histogram of oriented gradients (HOG) features to determine the target scale and deep features to determine the target location. In addition, considering the large inter-frame distance between targets in UAV videos, we use a saliency detection method to dynamically generate spatially constrained templates. The proposed DTSRT outperforms other state-of-the-art algorithms with area under curve (AUC) of 0.481, 0.431, and 0.474 on UAV123@10FPS, UAVDT, and DTB70 datasets, respectively.

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“…V ISUAL tracking based on UAV remote sensing videos is an important research direction of remote sensing [1]- [3]. Generally, UAV remote sensing videos are shot by onboard cameras at a higher flight altitude, which contain a variety of ground-based observation objects.…”

Section: Introductionmentioning

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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STN-Track: Multiobject Tracking of Unmanned Aerial Vehicles by Swin Transformer Neck and New Data Association Method

Cited by 20 publications

References 35 publications

Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Adaptive Spatial Regularization Correlation Filters for UAV Tracking

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

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