Real-time statistical image and video processing for remote sensing and surveillance applications

Khosravi, Mohammad R.; Tavallali, Pooya

doi:10.1007/s11554-021-01168-x

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…At the same time, the experimental data show that the system is 8.53 times faster than that of single node with 9 computing nodes. erefore, with the increase of processing nodes, the acceleration ratio of the system basically increases linearly, the total running time of the system increases less, and the system has good scalability [24,25].…”

Section: Extensibility Verification Of Spark Face Recognition Formentioning

confidence: 99%

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Ming

Xue

2022

Mathematical Problems in Engineering

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In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry.

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Section: Extensibility Verification Of Spark Face Recognition Formentioning

confidence: 99%

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Ming

Xue

2022

Mathematical Problems in Engineering

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Features extraction from multi-spectral remote sensing images based on multi-threshold binarization

Rusyn,

Lutsyk,

Kosarevych

et al. 2023

Sci Rep

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In this paper, we propose a solution to resolve the limitation of deep CNN models in real-time applications. The proposed approach uses multi-threshold binarization over the whole multi-spectral remote sensing image to extract the vector of discriminative features for classification. We compare the classification accuracy and the training time of the proposed approach with ResNet and Ensemble CNN models. The proposed approach shows a significant advantage in accuracy for small datasets, while keeping very close recall score to both deep CNN models for larger datasets. On the other hand, regardless of the dataset size, the proposed multi-threshold binarization provides approximately 5 times lower training and inference time than both ResNet and Ensemble CNN models.

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The Art System of Picture Composition Based on Computer Video Processing Technology

Zhu¹,

Wang²

2023

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)

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Real-time statistical image and video processing for remote sensing and surveillance applications

Cited by 4 publications

References 27 publications

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Video Image Processing Method Based on Cloud Platform Massive Data and Virtual Reality

Features extraction from multi-spectral remote sensing images based on multi-threshold binarization

The Art System of Picture Composition Based on Computer Video Processing Technology

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