A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

Chen, Ting; Pennisi, Andrea; Li, Zhi; Zhang, Yanning; Sahli, Hichem

doi:10.3390/rs10091347

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…However, false alarms may occur during target detection, which can result in the system choosing the wrong landing platform and fail to land. Therefore, the detection results are taken as the input, and the data association method is used [29][30][31][32][33] to obtain target tracking trajectories. Such a tracking algorithm can eliminate false alarms from the detection results and improve the locating performance of the target in the system.…”

Section: Wide Fov Detection and Trackingmentioning

confidence: 99%

Hybrid Camera Array-Based UAV Auto-Landing on Moving UGV in GPS-Denied Environment

et al. 2018

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With the rapid development of Unmanned Aerial Vehicle (UAV) systems, the autonomous landing of a UAV on a moving Unmanned Ground Vehicle (UGV) has received extensive attention as a key technology. At present, this technology is confronted with such problems as operating in GPS-denied environments, a low accuracy of target location, the poor precision of the relative motion estimation, delayed control responses, slow processing speeds, and poor stability. To address these issues, we present a hybrid camera array-based autonomous landing UAV that can land on a moving UGV in a GPS-denied environment. We first built a UAV autonomous landing system with a hybrid camera array comprising a fisheye lens camera and a stereo camera. Then, we integrated a wide Field of View (FOV) and depth imaging for locating the UGV accurately. In addition, we employed a state estimation algorithm based on motion compensation for establishing the motion state of the ground moving UGV, including its actual motion direction and speed. Thereafter, according to the characteristics of the designed system, we derived a nonlinear controller based on the UGV motion state to ensure that the UGV and UAV maintain the same motion state, which allows autonomous landing. Finally, to evaluate the performance of the proposed system, we carried out a large number of simulations in AirSim and conducted real-world experiments. Through the qualitative and quantitative analyses of the experimental results, as well as the analysis of the time performance, we verified that the autonomous landing performance of the system in the GPS-denied environment is effective and robust.

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Section: Wide Fov Detection and Trackingmentioning

confidence: 99%

Hybrid Camera Array-Based UAV Auto-Landing on Moving UGV in GPS-Denied Environment

et al. 2018

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“…SDE-based multi-object tracking algorithms use separate networks for object detection and re-identification feature extraction [12]- [17]. They improve tracking performance in remote sensing by using historical trajectory information and separate network extraction of identity embedding, as seen in [18]- [24]. However, these algorithms are complex and not suitable for real-time tracking.…”

Section: Introductionmentioning

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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“…Multiple object tracking (MOT) aims to predict the trajectories of all targets from a given video. With the development of computer vision, MOT is widely applied in many fields, such as intelligent video surveillance [1], human-computer interaction [2], and autonomous driving [3]. In addition, MOT is the foundation for advanced computer vision tasks such as video understanding [4], behavior recognition [5], and behavior analysis [6].…”

Section: Introductionmentioning

confidence: 99%

One-Shot Multiple Object Tracking in UAV Videos Using Task-Specific Fine-Grained Features

Nie

et al. 2022

Remote Sensing

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Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorporates detection and ReID in a unified network, has gained attention due to its fast inference speed. It significantly reduces the computational overhead by making two subtasks share features. However, most existing one-shot trackers struggle to achieve robust tracking in UAV videos. We observe that the essential difference between detection and ReID leads to an optimization contradiction within one-shot networks. To alleviate this contradiction, we propose a novel feature decoupling network (FDN) to convert shared features into detection-specific and ReID-specific representations. The FDN searches for characteristics and commonalities between the two tasks to synergize detection and ReID. In addition, existing one-shot trackers struggle to locate small targets in UAV videos. Therefore, we design a pyramid transformer encoder (PTE) to enrich the semantic information of the resulting detection-specific representations. By learning scale-aware fine-grained features, the PTE empowers our tracker to locate targets in UAV videos accurately. Extensive experiments on VisDrone2021 and UAVDT benchmarks demonstrate that our tracker achieves state-of-the-art tracking performance.

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A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

Cited by 6 publications

References 40 publications

Hybrid Camera Array-Based UAV Auto-Landing on Moving UGV in GPS-Denied Environment

Hybrid Camera Array-Based UAV Auto-Landing on Moving UGV in GPS-Denied Environment

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

One-Shot Multiple Object Tracking in UAV Videos Using Task-Specific Fine-Grained Features

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