High-performance UAVs visual tracking based on siamese network

Yang, Shuaidong; Chen, Haiyun; Xu, Fancheng; Yang, Li; Yuan, Jiemin

doi:10.1007/s00371-021-02271-7

Cited by 16 publications

(5 citation statements)

References 58 publications

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“…Pan et al (2019) used visual data captured by drones to track multiple objects [44]. In [45], the authors suggested a new DAFSiamRPN tracker for drone's object detection tracking based on SiamRPN, which addresses the problem that unmanned aerial vehicles (UAVs) are readily influenced by diverse surroundings in the object tracking process such as full occlusion variation of illumination. Xu et al (2018) proposed an improved YOLO model, which uses a small UAV to collect data at low altitudes, which significantly improves the ability to identify small targets [46].…”

Section: Machine Learning Model Based On Uav Datamentioning

confidence: 99%

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

Gao,

Bambrah,

Parihar

et al. 2024

Agriculture

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With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.

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Section: Machine Learning Model Based On Uav Datamentioning

confidence: 99%

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

Gao,

Bambrah,

Parihar

et al. 2024

Agriculture

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show abstract

“…It is assumed that a classic pinhole camera model facilitates the non-rigid 3D image acquisition process [12][13], allowing the non-rigid 3D image to be described as follows:…”

Section: A the Calculation Of Low-rank Matrices For Dynamic Shape Bas...mentioning

confidence: 99%

“…At this point, a constraint de 3 is obtained, describing the velocity changes in non-rigid body motion. (13) After this discussion, we have obtained the error de 1 between the points describing the transformation of the manifold motion group and the measured points as the main subject for minimizing. Additionally, we have two constraints de 2 and de 3 describing the displacement and velocity changes of parameters in the non-rigid body motion process.…”

Section: B Design Of a Strongly Reliable Objective Function For Non-r...mentioning

confidence: 99%

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Chen,

Moqurrab

2024

IEEE Access

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Most reconstruction algorithms for non-rigid three-dimensional (3D) images assume that non-rigidity can be represented as a linear combination of a fixed number of rigid bases. However, this assumption struggles to establish reliable shape functions and initial values for nonlinear and non-rigid motions, decreasing reconstruction reliability. This paper introduces an enhanced-reliability reconstruction algorithm for non-rigid 3D images. Our algorithm models the dynamic non-rigid shape basis as a low-rank matrix composed of image points and depth factors, improving the restoration of non-rigid shape base changes and providing accurate parameters for constructing objective functions. By leveraging manifold alignment and physical continuity constraints, our method optimizes the function structures. Assuming minimal reconstruction error and shape change, we solve for the motion structure parameters and select the key initial shape basis value by minimizing the objective function with the L-M nonlinear optimization method. Our experimental results on 3D image sequence reconstructions demonstrate significant error reduction, underscoring our model's credibility, robust reliability, and minimal re-projection error.

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“…Pan S et al (2019) use visual data captured by drones to track multiple objects [42]. In [43], authors have suggested a new DAFSiamRPN tracker for drone's object detection tracking based on SiamRPN which addresses the problem that (unmanned aerial vehicles) UAVsare readily influenced by diverse surroundings in the object tracking process such as full occlusion variation of illumination. Xu et al (2018) proposed an improved YOLO model, which uses a small UAV to collect data at low altitude, which significantly improves the ability to identify small targets [44].…”

Section: Machine Learning Model Based On Uav Datamentioning

confidence: 99%

Building Smart Surveillance Solutions for Livestock Farms

Gao

Bambrah

Parihar

et al. 2023

Preprint

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Farming is an important industry in the world. With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce strong results. In order to achieve intelligent monitoring of agricultural activities to improve economic benefits, this paper combines UAV with Deep learning model, through the combination of UAV in the agricultural industry to detect and classify objects, to achieve independent agriculture without human intervention. In this paper, a highly reliable target detection and tracking system using UAV is developed, which will be proved to be cost-effective. The system uses Deep learning method to solve the target problem. The model uses the data collected from DJI Mirage 4 UAV to detect, track and classify different types of targets. The average map accuracy of this method for target detection and tracking is 90.98\%, the average accuracy is 89.76\%, and the average recall rate is 88.78\%. In addition, this paper also compares the performance of different Deep learning models, such as YOLOv7, FASTER-RCNN, SSD, MASK-RCNN, and compares them with key evaluation indicators, and finally determines a method through the development of YOLOv7-DeepSort model.

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High-performance UAVs visual tracking based on siamese network

Cited by 16 publications

References 58 publications

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Building Smart Surveillance Solutions for Livestock Farms

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