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Volumn 2018-July, Issue , 2018, Pages 6416-6419

Virtualot - A framework enabling real-time coordinate transformation & occlusion sensitive tracking using UAS products, deep learning object detection & traditional object tracking techniques

Author keywords

Computer vision; Homography; Object detection; Object tracking; Photogrammetry

Indexed keywords


EID: 85063155600     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2018.8518124     Document Type: Conference Paper
Times cited : (16)

References (10)
  • 1
    • 84880556243 scopus 로고    scopus 로고
    • A new monoplotting tool to extract georeferenced vector data and orthorectified raster data from oblique non-metric photographs
    • Swiss Federal Research Institute WSL, Insubric Ecosystem Research Group, CH-6500 Bellinzona, Switzerland
    • C. Bozzini, M. Conedera, and P. Krebs, “A new monoplotting tool to extract georeferenced vector data and orthorectified raster data from oblique non-metric photographs,” International Journal of Heritage in the Digital Era, vol. 1, no. 3, Swiss Federal Research Institute WSL, Insubric Ecosystem Research Group, CH-6500 Bellinzona, Switzerland, 2012.
    • (2012) International Journal of Heritage in the Digital Era , vol.1 , Issue.3
    • Bozzini, C.1    Conedera, M.2    Krebs, P.3
  • 2
    • 85063157941 scopus 로고    scopus 로고
    • An open tool to register landscape oblique images and and generate their synthetic model
    • Online.
    • T. Produit and D. Tuia, “An open tool to register landscape oblique images and and generate their synthetic model,” REMOTE SENSING & SPATIAL ANALYSIS, pp. 170-176, 2012. [Online]. Available: http://2012.ogrscommunity.org/2012 papers/d3 2 produit abstract.pdf
    • (2012) REMOTE SENSING & SPATIAL ANALYSIS , pp. 170-176
    • Produit, T.1    Tuia, D.2
  • 4
    • 85021846258 scopus 로고    scopus 로고
    • YOLO9000: Better, faster, stronger
    • U. of Washington, and A. I. for AI University of Washington; Allen Institute for AI, Online.
    • J. Redmon, A. Farhadi, U. of Washington, and A. I. for AI, “Yolo9000: Better, faster, stronger,” in Computer Vision and Pattern Recognition. University of Washington; Allen Institute for AI, 2016. [Online]. Available: https://arxiv.org/pdf/1612.08242
    • (2016) Computer Vision and Pattern Recognition
    • Redmon, J.1    Farhadi, A.2
  • 8
    • 0028112849 scopus 로고
    • Good features to track
    • Online.
    • J. Shi and C. Tomasi, “Good features to track,” Computer Vision and Pattern Recognition, 1994. [Online]. Available: http://www.ai.mit.edu/courses/6.891/handouts/shi94good.pdf
    • (1994) Computer Vision and Pattern Recognition
    • Shi, J.1    Tomasi, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.