Object tracking methods comparison

• Autorzy: Bujnowski Daniel , Giełczyk Agata

Warianty tytułu

PL

Porównanie metod śledzenia obiektów

• Języki publikacji: EN

Abstrakty

EN

Object tracking has been improved recently and now it seems to be one of the most challenging task in a computer vision area. In this article there are presented five top state-of-art algorithms. There were tested and the comparison of their results was performed and presented in plots and tables. A precision and an accuracy were evaluated, while some intruding factors, like rotations or blurring, were observed.

PL

Śledzenie obiektów jest coraz bardziej popularne i może być uznane za jedno z najbardziej wymagających zadań w obszarze wizji komputerowej. W pracy zaprezentowano pięć najbardziej wydajnych i najlepiej znanych algorytmów. Zostały one zaimplementowane, przetestowane i porównane. Wyniki tego porównania przedstawiono za pomocą wykresów oraz tabel. Podczas testów oceniane były precyzja i dokładność śledzenia. Obserwowano również wpływ czynników zakłócających na jakość śledzenia.

Słowa kluczowe

EN

object tracking; image processing; artificial intelligence

PL

śledzenie obiektu; przetwarzanie obrazu; sztuczna inteligencja

• Wydawca: Wydawnictwa Uczelniane Politechniki Bydgoskiej im. Jana i Jędrzeja Śniadeckich
• Czasopismo: Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
• Rocznik: 2017
• Tom: z. 20
• Strony: 15--24
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Bujnowski Daniel

• UTP University of Science and Technology, Faculty of Telecommunications, Computer Science and Electrical Engineering, Al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland

autor

Giełczyk Agata

• UTP University of Science and Technology, Faculty of Telecommunications, Computer Science and Electrical Engineering, Al. prof. S. Kaliskiego 7, 85-796 Bydgoszcz, Poland

Bibliografia

• [1] Babenko B., 2008. Multiple instance learning: algorithms and applications. View Artic. PubMedNCBI Google Sch.
• [2] Babenko B., Yang M.-H., Belongie S., 2009. Visual tracking with online multiple instance learning. [In:] Computer Vision and Pattern Recognition, CVPR 2009. IEEE Conference on. IEEE, 983-990.
• [3] Bandyopadhyay S., Ghosh D., Mitra R., Zhao Z., 2015. MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci. Rep. 5, 8004.
• [4] Coifman B., Beymer D., McLauchlan P., Malik J., 1998. A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. Part C Emerg. Technol. 6, 271-288 .
• [5] Dietterich T.G., Lathrop R.H., Lozano-Pérez T., 1997. Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89, 31-71.
• [6] Hare S., Golodetz S., Saffari A., Vineet V., Cheng M.-M., Hicks S.L., Torr P.H.S., 2016. Struck: Structured Output Tracking with Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2096-2109.
• [7] Hare S., Saffari A., Golodetz S., 2014. Struck: Structured Output Tracking with Kernels. IEEE Trans. PATTERN Anal. Mach. Intell.
• [8] Hare S., Saffari A., Torr P.H.S., 2011. Struck: Structured output tracking with kernels. Proc IEEE Int Conf Comput Vis. 263-270.
• [9] Henriques J.F., Caseiro R., Martins P., Batista J., 2012. Exploiting the circulant structure of tracking-by-detection with kernels. [In:] European conference on computer vision. Springer, 702-715.
• [10] Henriques J.F., Caseiro R., Martins P., Batista J., 2015. High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37, 583-596.
• [11] Kotzias D., Denil M., de Freitas N., Smyth P., 2015. From Group to Individual Labels Using Deep Features. Presented at the .
• [12] Nigam A., Tiwari K., Gupta P., 2016. Multiple texture information fusion for finger-knuckle-print authentication system. Neurocomputing. 188, 190-205.
• [13] Pestana J., Sanchez-Lopez J.L., Saripalli S., Campoy P., 2014. Computer vision based general object following for gps-denied multirotor unmanned vehicles. [In:] American Control Conference (ACC), IEEE, 1886-1891.
• [14] Sherawat H., Dalal S., 2016. Palmprint Recognition System Using 2-D Gabor and SVM as Classifier. IJITR. 4, 3007-3010.
• [15] Solis Montero A., Lang J., Laganiere R., 2015. Scalable kernel correlation filter with sparse feature integration. [In:] Proceedings of the IEEE International Conference on Computer Vision Workshops, 24-31.
• [16] Viola P., Jones M., 2001. Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on.. IEEE, I-I.
• [17] Visual Tracker Benchmark, http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html.
• [18] Yang H., Shao L., Zheng F., Wang L., Song Z., 2011. Recent zadvances and trends in visual tracking: A review. Neurocomputing. 74, 3823-3831.
• [19] Yang L., Georgescu B., Zheng Y., Wang Y., Meer P., Comaniciu D., 2011. Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking. IEEE Trans. Med. Imaging. 30, 1921-1932.
• [20] Zhang K., Zhang L., Yang M.-H., 2012. Real-time compressive tracking. [In:] European Conference on Computer Vision, Springer, 864-877.
• [21] Zhu G., Wang J., Wu Y., Lu H., 2015. Collaborative Correlation Tracking. [In:] BMVC, 184-1.
• [22] Zhu W., Lou Q., Vang Y.S., Xie X., 2016. Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification. ArXiv Prepr. ArXiv161205968.