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Queue Waiting Time Estimation Using Person Re-identification by Upper Body
P. 464–474.
Konushin A., Mamedov T., Kuplyakov D.
In this paper, we propose a new approach to estimating waiting time in queue based on object tracking and person re-identification by upper body. The task we are considering is practically important in video analysis, because this data can be used for predictive analytics and improvement of customer services. The main idea of the proposed method is to use upper body detections instead of full body detections. This decision is due to the following fact: in queues, the upper bodies are more visible. Using re-identification allows us to perform video analytics on sparse frames and thereby increase the computational efficiency of the estimation algorithm. Also in this work, we introduce a novel upper body regression by head, upper body random size augmentation to improve re-identification performance in real-world scenarios and a method for calculating metrics for queue waiting time estimation algorithms. Our experimental evaluation showed that the proposed algorithm has a high accuracy of queue waiting time estimation.
Keywords: computer vision
In book
Vol. 3027. , CEUR Workshop Proceedings, 2021.
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Added: May 29, 2026
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Added: December 12, 2025
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Added: November 24, 2025
Dalian: IEEE, 2025.
The increasing complexity of modern software development necessitates intelligent, automated security analysis frameworks that can effectively pay attention of human on high-risk software releases. This paper introduces a Multi Agent System (MAS) framework designed to enhance the security assessment process by leveraging artificial intelligence (AI) and intelligent computing for real-time release analysis. The proposed system ...
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Springer, 2025.
The multi-volume set LNCS 15623 until LNCS 15646 constitutes the proceedings of the workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, which took place in Milan, Italy, during September 29–October 4, 2024.
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Added: June 1, 2025
Generation of Artificial Images of Cross Sections of WC/Co Composite Alloys Using Diffusion Networks
Kagramanyan D., Shchur L., Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 3 P. 1315–1321
The study of statistical properties of microstructures of composite materials is carried out by analysing microphotographs of material cuts. The obtained two-dimensional structure represents cuts of composite elements, the geometrical properties of which can be studied by computer vision methods. Hundreds of micro-images can be collected from one microstructure, which allows to study statistical properties ...
Added: January 13, 2025
Springer, 2024.
Added: December 9, 2024
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Springer, 2024.
The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024.
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Added: November 26, 2024
Sergeev A., Minchenkov V., Солдатов А. В. et al., / Cornell University. Серия Computer Science "arxiv.org". 2024. № 2411.10150.
Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to variations in environmental conditions ...
Added: November 23, 2024
Cham: Springer, 2024.
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Prutyanov V., Тернов М. А., Костров Д. С., , in: 2024 International Russian Automation Conference (RusAutoCon).: IEEE, 2024.
The work presents an analysis of the application of deep learning-based methods for the keypoint extraction and matching in the context of map-aided UAV visual localization. A method for visual localization in three degrees of freedom is proposed, which employs pretrained SuperPoint and LightGlue networks for both short-term optical flow and global frame-to-map matching components. ...
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Проворова А. А., Polyakova I., Kuzmicheva E., Научная визуализация 2024 Т. 16 № 3 С. 1–13
Machine methods of image analysis are gaining popularity in various fields of life. However, the question remains as to how effective such algorithms are on low-quality data, such as those that can be used in the field of telemedicine. The work provides a comparative analysis of various approaches to object detection in MRI brain images ...
Added: August 9, 2024
IEEE, 2024.
Added: July 10, 2024
Meshcheryakov R., Kataev M., Pantiukhin D., , in: Integral Robot Technologies and Speech Behavior.: Newcastle upon Tyne: Cambridge Scholars Publishing, 2024. Ch. 4 P. 130–154.
For a robot that functions and performs its mission, it is important to receive information from various sources that are increasingly being called sensors. When drawing an analogy between a robot and a living being, the same comparison can be made for obtaining information about the world from various sources. Often, a computer vision system ...
Added: December 10, 2023
Kharlamov A. A., Pantiukhin D., Borisov V. et al., Newcastle upon Tyne: Cambridge Scholars Publishing, 2024.
The monograph presents papers on the subject domain “Integral robot. Speech behavior”. These cover issues of a theoretical nature, including representation and processing of speech information in the human mind in the process of both text analysis and text generation, and specifically the need to use jointly working linguistic and extralinguistic models of the world, ...
Added: December 1, 2023
Cham: Springer, 2023.
Added: November 29, 2023
Berezovskiy V., Morozov N., , in: The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023.: [б.и.], 2023.
Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widely known that KD can offer an improvement to student generalization in i.i.d setting, its performance under domain shift, i.e. the ...
Added: November 20, 2023
The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023
[б.и.], 2023.
Deep learning models are usually developed and tested under the implicit assumption that the training and test data are drawn independently and identically distributed (IID) from the same distribution. Overlooking out-of-distribution (OOD) images can result in poor performance in unseen or adverse viewing conditions, which is common in real-world scenarios. In this workshop, we are ...
Added: November 20, 2023
IEEE, 2023.
Computer Vision (ICCV), 2023 IEEE International Conference on ...
Added: November 5, 2023