Towards long‐term UAV object tracking via effective feature matching

Zhao, Baojun; Wang, Hongshuo; Tang, Linbo; Han, Yan

doi:10.1049/el.2020.1096

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concurrently, the proliferation of low-altitude aerial vehicles, notably UAVs, has rapidly developed, complicating airport safety due to unauthorized UAV intrusions [2]. Additionally, drones now have widespread applications in many fields [3], and applications based on drone detection and tracking [4][5][6][7][8] also have a wide range of demands in civilian security and military reconnaissance. However, with the rapid increase in the number of drones, their flight safety also needs to be given attention.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

Liang,

Cen

2024

Remote Sensing

View full text Add to dashboard Cite

In the realm of short-range radar applications, the focus on detecting “low, slow, and small” (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

Liang,

Cen

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The main purpose of this technology is to search for the best matching position of the real image in the reference image and provide basic data for the position change of the carrier [22,23]. Existing image-matching algorithms are mainly divided into grayscale-based and featurebased image-matching methods [24][25][26][27][28]. Grayscale-based image matching takes the template image as a sliding window image, slides through the image in a sequence according to a certain step size, and selects the part with the largest similarity as the final result.…”

Section: Introductionmentioning

confidence: 99%

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Fan,

Yang,

et al. 2023

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

The positioning function of unmanned aerial vehicles (UAVs) is a challenging and fundamental research topic and is the premise for UAVs to realize autonomous navigation. The disappearance of satellite signals makes it challenging to achieve accurate positioning. Thus, visual positioning algorithms based on computer vision have been proposed in recent years and these algorithms have produced good results. However, these algorithms have relatively simple functions and cannot perceive the environment. Their versatility is poor, and mismatching often occurs, which affects the positioning accuracy. Aiming to address the need for integrated target recognition, target matching, and positioning of UAVs, we propose an algorithm that integrates the target recognition, matching, and positioning functions by combining the single-shot multibox detector (SSD) algorithm with the deep feature matching algorithm. This algorithm is based on the idea of pseudo-Siamese networks and the SSD algorithm, introducing a deep feature matching method to directly calculate the correspondence between two images. The main idea is to use the VGG network trained by the SSD target recognition algorithm to extract deep features, without any special training for feature matching. Finally, by sharing neural network weights, the integrated design of target recognition and image-matching localization algorithms is achieved. Mismatches between the real-time and reference images are addressed by introducing the grid-based motion statistics algorithm to optimize the matching result and improve the correct matching efficiency of the target. The University-Release dataset was used to compare and analyze the performance of the proposed algorithm to verify its superiority and feasibility. The results show that the matching accuracy of the PSiamRML algorithm is generally good and that it significantly compensates for changes in the contrast, scale, brightness, blur, deformation, and so on, apart from improving the stability and robustness. Finally, a matching test scenario with aerial images captured by an S1000 six-rotor UAV served to verify the effectiveness and practicability of the PSiamRML algorithm.

show abstract

“…Viewpoint Variation and Occlusion: Due to the UAV platforms having the characteristics of large freedom and mobility degrees, UAVs might capture the targets from different aspects by flying around the targets by 360°. For example, UAVs can capture the back or front side of the targets, in which case the targets may have severe variations in the imaging process [13]. This will become a big challenge if the methods do not have the ability for timely online learning and model updates.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Han

Liu

Wang

et al. 2022

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

Self Cite

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) have been widely applied in military and civilian fields due to their flexibility and effectiveness. As a vital component of UAVs, the vision system has taken on great significance in different applications (e.g., autonomous landing, traffic surveillance, and disaster rescue) to attract widespread attention in recent years. Therefore, the automatic understanding of visual data collected from these air platforms becomes urgently needed in UAV systems. In this review, we revisit and summarize the recent techniques and developments for several typical UAV applications, including object detection, object tracking, and semantic segmentation. In addition, we also highlight the difficulties and subsequent orientations from different perspectives, which may stimulate future research and applications in the UAV vision era.

show abstract

Towards long‐term UAV object tracking via effective feature matching

Cited by 5 publications

References 16 publications

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

Radar Signal Classification with Multi-Frequency Multi-Scale Deformable Convolutional Networks and Attention Mechanisms

PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle Platform

Contact Info

Product

Resources

About