Mean Shift Tracker With Grey Prediction for Visual Object Tracking

Mingming, Lv; Wang, Li; Hou, Yuanlong; Gao, Qiang; Hou, Runmin

doi:10.1109/cjece.2018.2875142

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Other possibilities are last observation, median, mode, low pass filter, k-nearest neighbors, weighted average and regression analysis. The mean predictor has been used by different researchers and has proved to be reasonably accurate [53][54][55]. Different types of predictors: a mean, a linear regression, an inverse regression and a logarithmic regression, were evaluated by these authors; their work also found that the mean is the single best predictor.…”

Section: Similarity Measurementioning

confidence: 99%

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

et al. 2022

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Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.

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Section: Similarity Measurementioning

confidence: 99%

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

et al. 2022

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“…In the mean-shift tracker (MST) (Lv et al , 2018) method, the target model and candidate image patches are modeled with color histograms. MST gives successful results against partial occlusion and scale changes.…”

Section: Visual Target Detection and Trackingmentioning

confidence: 99%

Small aircraft detection using deep learning

Kiyak

Unal

2021

AEAT

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Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

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“…Lyu M.M. et al [22][23][24][25] proposed a series of miss-distance time-delay control methods for remotely operated weapon station platforms. The tests showed that the overshoot decreased to 2.5%.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System

Shen,

Wen,

Zhu

et al. 2024

Axioms

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Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method.

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Mean Shift Tracker With Grey Prediction for Visual Object Tracking

Cited by 5 publications

References 18 publications

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

Small aircraft detection using deep learning

Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System

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