Методи відстеження довільної кількості об’єктів у реальному часі на мобільній платформі

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dc.citation.epage59
dc.citation.issue1
dc.citation.journalTitleКомп'ютерні системи та мережі
dc.citation.spage50
dc.citation.volume5
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorКушнір, Д. О.
dc.contributor.authorKushnir, D.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-07-23T09:11:13Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractРозглянуто проблему вибору методів відстеження розпізнаних об’єктів (ВО) у реальному часі для систем з обмеженими апаратними можливостями. Визначено, що для таких систем доречна інтеграція методів відстеження у пристрій, оминаючи використання передавання даних через мережу інтернет. Розглянуто відомі методи відстеження довільної кількості об’єктів у реальному часі. Серед метрик оцінювання ефективності використано: MOTA, MOTP, F1, MT, ML, ID, FM. На основі аналізу ефективності таких методів за згаданими вище метриками запропоновано використати метод відстеження V-IOU для відстеження розпізнаних об’єктів на мобільній платформі у мобільній кіберфізичній системі.
dc.description.abstractThe problem of choosing methods for tracking recognized objects in real-time for systems with limited hardware capabilities is considered. It was determined that for such scenarios, it is appropriate to integrate tracking methods into the device, bypassing data transmission via the Internet. Existing methods of tracking an arbitrary number of objects in real-time are considered. Among the performance evaluation metrics, the following were used: MOTA, MOTP, F1, MT, ML, ID and FM. Based on the primary analysis of the effectiveness of such methods according to the metrics mentioned above, it was proposed to use the V-IOU tracking method to track recognized objects on a mobile platform in a mobile cyber-physical system.
dc.format.extent50-59
dc.format.pages10
dc.identifier.citationКушнір Д. О. Методи відстеження довільної кількості об’єктів у реальному часі на мобільній платформі / Д. О. Кушнір // Комп'ютерні системи та мережі. — Львів : Видавництво Львівської політехніки, 2023. — Том 5. — № 1. — С. 50–59.
dc.identifier.citationenKushnir D. Methods of tracking an arbitrary number of objects in real-time on a mobile platform / D. Kushnir // Computer Systems and Networks. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 5. — No 1. — P. 50–59.
dc.identifier.doidoi.org/10.23939/csn2023.01.050
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/111641
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofКомп'ютерні системи та мережі, 1 (5), 2023
dc.relation.ispartofComputer Systems and Networks, 1 (5), 2023
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dc.relation.references19. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831. https://doi.org/10.48550/arXiv.1603.00831.
dc.relation.references20. Kushnir, D., & Paramud, Y. (2020). The algorithm of Cyber-Physical system targeting on a movable object using the smart sensor unit. Advances In Cyber-Physical Systems, 5(1), 16–22. https://doi.org/10.23939/acps2020.01.016.
dc.relation.referencesen1. Hambling, D. (2023). Drones killing without oversight? New Scientist, 260(3461), 1–8. https://doi.org/10.1016/S0262-4079(23)01937-1.
dc.relation.referencesen2. Kushnir, D. (2023). Methods and means of searching and recognizing objects in video images on the mobile platform in real-time [Doctoral dissertation, Lviv Polytechnic National University]. Institutional repository of Lviv Polytechnic National University. https://lpnu.ua/sites/default/files/2023/radaphd/23565/diskushnir.pdf
dc.relation.referencesen3. Niu, W., Ma, X., Lin, S., Wang, S., Qian, X., Lin, X., ... & Ren, B. (2020). Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, 907–922. https://doi.org/10.1145/3373376.3378534.
dc.relation.referencesen4. Thakkar, M. (2019). Beginning machine learning in iOS: CoreML Framework. Apress. https://doi.org/10.1007/978-1-4842-4297-1.
dc.relation.referencesen5. Dewantoro, G., Mansuri, J., & Setiaji, F. D. (2021). Comparative Study of Computer Vision Based Line Followers Using Raspberry Pi and Jetson Nano. Jurnal Rekayasa Elektrika, 17(4). https://doi.org/10.17529/jre.v17i4.21324.
dc.relation.referencesen6. Puchtler, P., & Peinl, R. (2020, September). Evaluation of deep learning accelerators for object detection at the edge. In German Conference on Artificial Intelligence (Künstliche Intelligenz). Springer, Cham. 320–326. https://doi.org/10.1007/978-3-030-58285-2_29.
dc.relation.referencesen7. Parmar, M. (2016). A survey of video object tracking methods. IJEDR. 4(1). 519–524. https://www.ijedr.org/papers/IJEDR1601086.pdf.
dc.relation.referencesen8. Kushnir, D. (2022) Methods and means for small dynamic objects recognition and tracking. Computers, Materials & Continua, 73(2), 3649–3655. https://doi.org/10.32604/cmc.2022.030016.
dc.relation.referencesen9. Patel, S. K., & Mishra, A. (2013). Moving object tracking techniques: A critical review. Indian Journal of Computer Science and Engineering, 4(2), 95–102. https://dx.doi.org/10.2139/ssrn.3548453.
dc.relation.referencesen10. Taufique, A. M. N., Minnehan, B., & Savakis, A. (2020). Benchmarking deep trackers on aerial videos. Sensors, 20(2), 547. https://doi.org/10.3390/s20020547.
dc.relation.referencesen11. Zhang, X., Chen, X., Sun, W., & He, X. (2021). Vehicle Re-Identification Model Based on Optimized DenseNet121 with Joint Loss. Cmc-computers materials & continua, 67(3), 3933–3948. https://doi.org/10.32604/cmc.2021.016560.
dc.relation.referencesen12. Sun, W., Dai, L., Zhang, X., Chang, P., & He, X. (2022). RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring. Applied Intelligence, 52(8), 8448–8463. https://doi.org/10.1007/s10489021-02893-3.
dc.relation.referencesen13. Wu, M., Cao, X., & Guo, S. (2020). Accurate detection and tracking of ants in indoor and outdoor environments. bioRxiv. https://doi.org/10.1101/2020.11.30.403816.
dc.relation.referencesen14. Bochinski, E., Senst, T., & Sikora, T. (2018, November). Extending IOU based multi-object tracking by visual information. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) IEEE, 1–6. https://doi.org/ 10.1109/AVSS.2018.8639144.
dc.relation.referencesen15. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831. https://doi.org/10.48550/arXiv.1603.00831.
dc.relation.referencesen16. Panteleev, O., & Oliynik, V. (2018). Method of visual multitracking in real time based on correlation filters. Adaptive systems of automatic control, 1(32), 97–106. https://doi.org/10.20535/1560-8956.32.2018.145620.
dc.relation.referencesen17. Wojke, N., & Bewley, A. (2018, March). Deep cosine metric learning for person re-identification. In 2018 IEEE winter conference on applications of computer vision (WACV). IEEE. 748–756. https://doi.org/10.1109/WACV.2018.00087.
dc.relation.referencesen18. Kushnir, D., & Paramud, Y. (2020). The inteligense algorithm of Cyber-Physical system targeting on a movable object using the smart sensor unit. Computer Systems And Networks, 2(1), 44–52. https://doi.org/10.23939/csn2020.01.044.
dc.relation.referencesen19. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831. https://doi.org/10.48550/arXiv.1603.00831.
dc.relation.referencesen20. Kushnir, D., & Paramud, Y. (2020). The algorithm of Cyber-Physical system targeting on a movable object using the smart sensor unit. Advances In Cyber-Physical Systems, 5(1), 16–22. https://doi.org/10.23939/acps2020.01.016.
dc.relation.urihttps://doi.org/10.1016/S0262-4079(23)01937-1
dc.relation.urihttps://lpnu.ua/sites/default/files/2023/radaphd/23565/diskushnir.pdf
dc.relation.urihttps://doi.org/10.1145/3373376.3378534
dc.relation.urihttps://doi.org/10.1007/978-1-4842-4297-1
dc.relation.urihttps://doi.org/10.17529/jre.v17i4.21324
dc.relation.urihttps://doi.org/10.1007/978-3-030-58285-2_29
dc.relation.urihttps://www.ijedr.org/papers/IJEDR1601086.pdf
dc.relation.urihttps://doi.org/10.32604/cmc.2022.030016
dc.relation.urihttps://dx.doi.org/10.2139/ssrn.3548453
dc.relation.urihttps://doi.org/10.3390/s20020547
dc.relation.urihttps://doi.org/10.32604/cmc.2021.016560
dc.relation.urihttps://doi.org/10.1007/s10489021-02893-3
dc.relation.urihttps://doi.org/10.1101/2020.11.30.403816
dc.relation.urihttps://doi.org/
dc.relation.urihttps://doi.org/10.48550/arXiv.1603.00831
dc.relation.urihttps://doi.org/10.20535/1560-8956.32.2018.145620
dc.relation.urihttps://doi.org/10.1109/WACV.2018.00087
dc.relation.urihttps://doi.org/10.23939/csn2020.01.044
dc.relation.urihttps://doi.org/10.23939/acps2020.01.016
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.rights.holder© Кушнір Д. О., 2023
dc.subjectV-IOU
dc.subjectвідстеження довільного класу об’єктів
dc.subjectмобільна платформа
dc.subjectреальний час
dc.subjectMOTA
dc.subjectMOTP
dc.subjectкіберфізична система
dc.subjectV-IOU
dc.subjectArbitrary object class tracking
dc.subjectMobile platform
dc.subjectReal-time
dc.subjectMOTA
dc.subjectMOTP
dc.subjectCyber-physical system
dc.subject.udc004.415.2
dc.titleМетоди відстеження довільної кількості об’єктів у реальному часі на мобільній платформі
dc.title.alternativeMethods of tracking an arbitrary number of objects in real-time on a mobile platform
dc.typeArticle

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Комп'ютерні системи та мережі. – 2023. – Том 5, № 1